Publish quality-filtered auxiliary training release

#1
by xuxuxuxuxu - opened
Files changed (48) hide show
  1. .gitattributes +1 -0
  2. README.md +120 -68
  3. archives/annotations-00000.tar +2 -2
  4. archives/images-child-00000.tar +2 -2
  5. archives/images-child-00001.tar +2 -2
  6. archives/images-child-00002.tar +2 -2
  7. archives/images-parent-00000.tar +2 -2
  8. archives/images-parent-00001.tar +2 -2
  9. archives/images-parent-00002.tar +2 -2
  10. archives/images-parent-00003.tar +2 -2
  11. archives/images-parent-00004.tar +2 -2
  12. archives/images-parent-00005.tar +2 -2
  13. archives/images-parent-00006.tar +2 -2
  14. archives/images-parent-00007.tar +2 -2
  15. archives/images-parent-00008.tar +2 -2
  16. archives/images-parent-00009.tar +2 -2
  17. archives/images-parent-00010.tar +2 -2
  18. package/FILTER_RESULTS.md +19 -0
  19. package/METHODS.md +216 -0
  20. package/README.md +72 -196
  21. package/audit/row_decisions.jsonl +3 -0
  22. package/index.json +69 -64
  23. package/manifest.jsonl +2 -2
  24. package/metadata/unfiltered_export_index.json +148 -0
  25. package/metadata/unfiltered_export_validation.json +87 -0
  26. package/quality_filter_policy.json +75 -0
  27. package/quality_filter_report.json +422 -0
  28. package/quality_validation.json +29 -0
  29. package/review/bcss_excluded.jpg +3 -0
  30. package/review/bcss_kept.jpg +3 -0
  31. package/review/lizard_excluded.jpg +3 -0
  32. package/review/lizard_kept.jpg +3 -0
  33. package/review/low_focus_candidates.json +0 -0
  34. package/review/midogpp_kept.jpg +3 -0
  35. package/review/nucls_excluded.jpg +3 -0
  36. package/review/nucls_kept.jpg +3 -0
  37. package/review/nuinsseg_excluded.jpg +3 -0
  38. package/review/nuinsseg_kept.jpg +3 -0
  39. package/review/pannuke_excluded.jpg +3 -0
  40. package/review/pannuke_kept.jpg +3 -0
  41. package/review/refpath_train_excluded.jpg +3 -0
  42. package/review/refpath_train_kept.jpg +3 -0
  43. package/review/samples.json +1222 -0
  44. package/sampling_config.json +13 -0
  45. package/storage_compaction.json +398 -0
  46. package/training_data.py +12 -1
  47. package/validation.json +61 -61
  48. release_manifest.json +259 -195
.gitattributes CHANGED
@@ -59,3 +59,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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  package/manifest.jsonl filter=lfs diff=lfs merge=lfs -text
 
 
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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  package/manifest.jsonl filter=lfs diff=lfs merge=lfs -text
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+ package/audit/row_decisions.jsonl filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -6,70 +6,70 @@ tags:
6
  - visual-grounding
7
  - auxiliary-training
8
  - parent-child
9
- pretty_name: PathGrounding - ZoomPath Auxiliary Training v4
10
  ---
11
 
12
  # PathGrounding
13
 
14
- Private, portable auxiliary training data for single-step pathology image
15
- grounding. Each record includes a parent image, an exact child crop, its bounding
16
- box in parent coordinates, a text question, and organ/MPP/training-level metadata
17
- when available. There are **618,432 parent-child pairs from seven sources**, using
18
- **34,193 distinct parent images**.
19
 
20
- This is not the ZoomPath v4 benchmark, does not include raw WSIs, and does not
21
- inherit the benchmark's doctor review. Source licenses and restrictions remain
22
- in the package; RefPath is gated and is not assumed redistributable. Keep this
23
- repository private unless all upstream redistribution permissions are checked.
 
24
 
25
- ## Data
 
 
 
 
26
 
27
- | Source | Pairs |
 
 
28
  | --- | ---: |
29
- | RefPath train | 28,908 |
30
- | PanNuke | 187,831 |
31
- | NuInsSeg | 25,718 |
32
- | NuCLS | 35,848 |
33
- | BCSS | 6,153 |
34
- | Lizard | 325,966 |
35
  | MIDOGpp | 8,008 |
36
- | **Total** | **618,432** |
37
-
38
- `partition` is `training`. Original `source_split`/fold information is preserved;
39
- no new train/val/test split is imposed. Keep targets from the same parent together
40
- when defining a later split. Nominal MPP and training scale estimates are flagged
41
- as approximate, especially the heuristic 20x/40x assignment for RefPath.
42
 
43
- ## Download And Restore
 
 
 
 
44
 
45
- Python 3.10+ is required. Authenticate with an account that has repository access:
 
46
 
47
  ```bash
48
  pip install -U huggingface_hub pillow
49
  hf auth login
50
- hf download xuxuxuxuxu/PathGrounding \
51
- --repo-type dataset --local-dir /data/pathgrounding-download
52
- python /data/pathgrounding-download/tools/restore_package.py \
53
- /data/pathgrounding-download /data/zoompath_training_v4
54
  ```
55
 
56
- Alternatively, call `huggingface_hub.snapshot_download` with the same repository,
57
- `repo_type="dataset"`, and `local_dir="/data/pathgrounding-download"`, then run
58
- the restore command. Interrupted downloads can be resumed.
59
-
60
- The images and per-parent annotations are stored in uncompressed TAR shards
61
- (approximately 2 GiB each) rather than 686,000 individual repository files.
62
- Restoration verifies every shard's SHA-256 checksum and recreates the original
63
- relative paths. It does not resize images or rewrite annotations/questions.
64
- Restoration can be rerun after interruption; it rewrites image/annotation files
65
- from the verified archives. Use a dedicated output directory.
66
-
67
- The data package is approximately 54 GB. Allow roughly 115 GB of free space to
68
- retain both downloaded archives and the restored package.
69
-
70
- ## Training Reader
71
 
72
  ```bash
 
 
73
  export PYTHONPATH=/data/zoompath_training_v4:$PYTHONPATH
74
  ```
75
 
@@ -79,37 +79,89 @@ from training_data import AuxiliaryTrainingReader
79
  reader = AuxiliaryTrainingReader("/data/zoompath_training_v4")
80
  row = next(reader.iter_records(dataset="refpath_train"))
81
  sample = reader.load_pair(row)
82
-
83
- parent = sample["parent_image"]
84
- child = sample["child_image"]
85
  question = sample["question"]
86
  selected_box = sample["child_bbox_xyxy_in_parent"]
87
- all_valid_boxes = sample["valid_target_bboxes_xyxy_in_parent"]
 
88
  organ, mpp, level = sample["organ"], sample["mpp"], sample["level"]
89
  ```
90
 
91
- Coordinates are integer pixel XYXY, top-left origin, half-open edges. The child
92
- is exactly the selected parent crop. `level` is `high`/`mid`/`low`, not a WSI
93
- pyramid index; MPP is in microns per pixel. Unsupported metadata remains null.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
94
 
95
- For multi-target training, use **all valid boxes**, not every box in the parent
96
- annotation file and not just the selected box. The reader applies the exact
97
- label/expression filter and checks `target_count`. Unselected matching targets
98
- are valid alternatives, not negatives. Stream `manifest.jsonl`; row-uniform
99
- sampling is not parent/query-uniform sampling.
100
 
101
  ## Layout And Validation
102
 
103
  | Repository path | Contents |
104
  | --- | --- |
105
- | `package/` | Manifest, index, reader, documentation, validation and source metadata |
106
- | `archives/` | Parent images, child crops and all per-parent annotations |
107
- | `tools/restore_package.py` | Checksum verifier and portable restoration tool |
108
- | `release_manifest.json` | File checksums, sizes, archive inventories and source counts |
109
-
110
- The restored package's `README.md` documents the full schema, provenance,
111
- approximate scale mapping, valid-target semantics and source restrictions.
112
- `validation.json` reports the completed export audit: all rows checked for
113
- structure/metadata and 700 deterministic crop pixel samples, with zero errors.
114
- The original source audit is preserved separately; the 700-sample export audit
115
- is not a claim that every exported image was rechecked pixel-by-pixel.
 
 
 
 
 
 
 
 
 
 
6
  - visual-grounding
7
  - auxiliary-training
8
  - parent-child
9
+ pretty_name: PathGrounding - Quality-Filtered Auxiliary Training
10
  ---
11
 
12
  # PathGrounding
13
 
14
+ Public, portable auxiliary training data for single-step pathology grounding.
15
+ The main package contains **235,346 quality-filtered parent-child pairs from
16
+ seven sources**, with **38,836 distinct parent images**. It replaces the earlier
17
+ unfiltered 618,432-pair main release.
 
18
 
19
+ **SegPath is a separate, optional package under `segpath/`: 2,193,058 pairs and
20
+ 105,864 parents.** It is not merged into the main manifest, counts, reader or
21
+ sampling configuration. It is disabled by default. Download it explicitly only
22
+ when needed; its mostly small semantic-region boxes differ from the larger
23
+ region-of-interest navigation targets in ZoomPath v4.
24
 
25
+ These are auxiliary training data, not the ZoomPath v4 benchmark. They contain no
26
+ raw WSIs and have not inherited the benchmark's doctor/pathologist review.
27
+ Upstream licenses and restrictions apply; RefPath is gated and redistribution
28
+ permission is not assumed; users must follow all relevant upstream terms.
29
+ SegPath is CC-BY-NC-SA 4.0.
30
 
31
+ ## Main Package
32
+
33
+ | Source | Retained pairs |
34
  | --- | ---: |
35
+ | BCSS | 2,836 |
36
+ | Lizard | 31,446 |
 
 
 
 
37
  | MIDOGpp | 8,008 |
38
+ | NuCLS | 32,538 |
39
+ | NuInsSeg | 20,521 |
40
+ | PanNuke | 111,605 |
41
+ | RefPath train | 28,392 |
42
+ | **Main total, excluding SegPath** | **235,346** |
 
43
 
44
+ Each record includes parent/child images, an exact parent-relative child box,
45
+ text question, organ and available scale metadata. `partition` is `training`;
46
+ original source split/fold information is retained, with no new train/val/test
47
+ split. Keep all related targets/parents, and source slides when identifiable,
48
+ together when defining later splits.
49
 
50
+ Python 3.10+ is required. Authenticate with an account that has repository access.
51
+ The explicit allow-list below does **not** download SegPath:
52
 
53
  ```bash
54
  pip install -U huggingface_hub pillow
55
  hf auth login
 
 
 
 
56
  ```
57
 
58
+ ```python
59
+ from huggingface_hub import snapshot_download
60
+
61
+ snapshot_download(
62
+ repo_id="xuxuxuxuxu/PathGrounding",
63
+ repo_type="dataset",
64
+ local_dir="/data/pathgrounding-download",
65
+ allow_patterns=["README.md", "release_manifest.json", "package/**",
66
+ "archives/**", "tools/restore_package.py"],
67
+ )
68
+ ```
 
 
 
 
69
 
70
  ```bash
71
+ python /data/pathgrounding-download/tools/restore_package.py \
72
+ /data/pathgrounding-download /data/zoompath_training_v4
73
  export PYTHONPATH=/data/zoompath_training_v4:$PYTHONPATH
74
  ```
75
 
 
79
  reader = AuxiliaryTrainingReader("/data/zoompath_training_v4")
80
  row = next(reader.iter_records(dataset="refpath_train"))
81
  sample = reader.load_pair(row)
82
+ parent, child = sample["parent_image"], sample["child_image"]
 
 
83
  question = sample["question"]
84
  selected_box = sample["child_bbox_xyxy_in_parent"]
85
+ valid_boxes = sample["valid_target_bboxes_xyxy_in_parent"]
86
+ ignored_boxes = sample["ignored_target_bboxes_xyxy_in_parent"]
87
  organ, mpp, level = sample["organ"], sample["mpp"], sample["level"]
88
  ```
89
 
90
+ The main images and annotations are uncompressed TAR shards, approximately
91
+ 2 GiB each. Restoration verifies SHA-256 and recreates relative paths without
92
+ resizing or changing questions. It is resumable by rerunning the command.
93
+ Use a dedicated output directory and allow space for both the downloaded shards
94
+ and the restored package. Exact byte totals are in `release_manifest.json`.
95
+
96
+ Coordinates are integer pixel XYXY, top-left origin, half-open edges. Children
97
+ are exact crops. For multi-target supervision, use all eligible `valid_boxes`,
98
+ not every annotated box or only the selected one. Source-supported answers
99
+ rejected by quality filtering remain `ignored_boxes`, never negatives.
100
+ The reader checks the exact label/expression and target count. Stream JSONL;
101
+ uniform row sampling is not uniform parent/query sampling.
102
+
103
+ `level` means the training category `high`/`mid`/`low`, not a WSI pyramid index.
104
+ MPP is microns per pixel; nominal RefPath magnification/MPP are approximate and
105
+ explicitly flagged. Unsupported metadata remains null.
106
+
107
+ ## Optional SegPath
108
+
109
+ Download only the independent SegPath package:
110
+
111
+ ```python
112
+ from huggingface_hub import snapshot_download
113
+
114
+ snapshot_download(
115
+ repo_id="xuxuxuxuxu/PathGrounding",
116
+ repo_type="dataset",
117
+ local_dir="/data/pathgrounding-download",
118
+ allow_patterns=["segpath/**", "tools/verify_segpath_package.py"],
119
+ )
120
+ ```
121
+
122
+ ```bash
123
+ python /data/pathgrounding-download/tools/verify_segpath_package.py \
124
+ /data/pathgrounding-download/segpath
125
+ export PYTHONPATH=/data/pathgrounding-download/segpath:$PYTHONPATH
126
+ ```
127
+
128
+ Use `AuxiliaryTrainingReader("/data/pathgrounding-download/segpath")` in a
129
+ separate reader/import context from the main package. The SegPath reader loads
130
+ images and annotations directly from eight native, uncompressed marker TARs;
131
+ no extraction is required. Reserve approximately 210 GiB plus download-cache
132
+ headroom. `segpath/release_manifest.json` gives the exact total and checksums.
133
+
134
+ Only official training patches are included. Targets are 8-connected semantic
135
+ mask regions, **not cell instances or WSI navigation paths**. Questions use full
136
+ labels. Known organs are retained; unknown organs are null. All rows record 40x,
137
+ approximate MPP `[0.25, 0.25]`, and training level `high`.
138
 
139
+ SegPath's `sampling_config.json` sets `enabled_by_default: false` and
140
+ `default_mix_probability: 0.0`. No combined manifest is provided. An explicitly
141
+ chosen future experiment should cap its contribution and sample marker, then
142
+ parent/query, then target, rather than concatenate rows and sample uniformly.
 
143
 
144
  ## Layout And Validation
145
 
146
  | Repository path | Contents |
147
  | --- | --- |
148
+ | `package/` | Main manifest, index, reader, quality policy, decisions, reports and provenance |
149
+ | `archives/` | Main parent/child images and per-parent annotations |
150
+ | `release_manifest.json` | Main-only inventory, counts, sizes and SHA-256 checksums |
151
+ | `tools/restore_package.py` | Main package checksum verification and restoration |
152
+ | `segpath/` | Independent optional SegPath manifest, index, TARs, reader and provenance |
153
+ | `segpath/release_manifest.json` | SegPath-only inventory, counts, sizes and checksums |
154
+ | `tools/verify_segpath_package.py` | SegPath checksums and sample loading check |
155
+
156
+ Both packages report `pass` with zero errors in their own `validation.json` and
157
+ `quality_validation.json`. The main export checks all 235,346 rows and 38,836
158
+ annotation views, with 700 deterministic crop-pixel samples. SegPath checks all
159
+ 2,193,058 rows and 105,864 annotation views, with 1,000 deterministic crop-pixel
160
+ rechecks; its source audit reconstructs every retained parent's original mask.
161
+ These automated audits are not a claim of a new manual or physician review.
162
+
163
+ The main package preserves historical source metadata showing SegPath excluded
164
+ from that build; this is intentional and does not invalidate the independent
165
+ validated `segpath/` release. Read each package's methods, quality policy,
166
+ filter results and provenance before training. Pin a Hub commit revision for
167
+ reproducible downloads.
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package/FILTER_RESULTS.md ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Quality Filter v1 Results
2
+
3
+ Status: automatically filtered; no manual/doctor review claimed.
4
+
5
+ | Dataset | Before | Retained | Excluded | Retained % |
6
+ | --- | ---: | ---: | ---: | ---: |
7
+ | bcss | 6,153 | 2,836 | 3,317 | 46.09 |
8
+ | lizard | 325,966 | 31,446 | 294,520 | 9.65 |
9
+ | midogpp | 8,008 | 8,008 | 0 | 100.00 |
10
+ | nucls | 35,848 | 32,538 | 3,310 | 90.77 |
11
+ | nuinsseg | 25,718 | 20,521 | 5,197 | 79.79 |
12
+ | pannuke | 187,831 | 111,605 | 76,226 | 59.42 |
13
+ | refpath_train | 28,908 | 28,392 | 516 | 98.22 |
14
+
15
+ Total: 235,346 / 618,432 retained pairs.
16
+ Parents: 38,836; annotation files: 38,836.
17
+
18
+ Overlapping reason counts and per-class counts are in quality_filter_report.json.
19
+ Every source row decision is in audit/row_decisions.jsonl.
package/METHODS.md ADDED
@@ -0,0 +1,216 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Auxiliary Training Quality Filter v1
2
+
3
+ This document records the deterministic quality filtering used to make a
4
+ training-oriented derivative of `zoompath_training_v4`. It is a methods record,
5
+ not a clinical validation claim. Generation does not overwrite its unfiltered
6
+ input. Optional post-validation storage compaction promotes the filtered package
7
+ to the canonical local name and retains the complete original in a separate
8
+ verified backup, with unfiltered export metadata preserved for lineage audits.
9
+
10
+ ## Scope
11
+
12
+ The input is the validated portable export with 618,432 parent-child pairs from
13
+ RefPath train, PanNuke, NuInsSeg, NuCLS, BCSS, Lizard and MIDOG++. The filter
14
+ operates on source pixel dimensions and source annotations. It does not change
15
+ the original questions, organs, MPP estimates, levels, source labels or source
16
+ licenses. It does not use the benchmark test set as training data.
17
+
18
+ ## Parent image checks
19
+
20
+ For every source parent image:
21
+
22
+ - minimum parent short side: 256 pixels;
23
+ - maximum parent aspect ratio: 4:1;
24
+ - reject a blank or nearly uniform parent using a 128-pixel thumbnail;
25
+ - verify the decoded parent pixels against the source manifest SHA-256.
26
+
27
+ An extreme-white pixel has all RGB channels >=245; an extreme-black pixel has
28
+ all channels <=10. The blank-image screen rejects a thumbnail if their union
29
+ covers at least 99.5% of pixels and its grayscale standard deviation is <=5,
30
+ or if grayscale standard deviation is <=1 and the 90th percentile of
31
+ `max(R,G,B)-min(R,G,B)` is <=5. This is an
32
+ automated corruption/empty-image screen, not a tissue adequacy or stain-quality
33
+ assessment. Blur and stain variation are measured for review candidates only;
34
+ they are not automatic exclusion criteria in v1.
35
+
36
+ ## Target checks
37
+
38
+ Cell/nucleus targets in PanNuke, NuInsSeg, NuCLS and Lizard require:
39
+
40
+ - short side at least 12 pixels;
41
+ - bbox area at least 256 pixels squared;
42
+ - bbox aspect ratio at most 4:1;
43
+ - target short side at least 8 pixels after a reference resize with long side
44
+ 1024 pixels;
45
+ - target bbox not touching a source parent boundary;
46
+ - when a source instance mask area exists, mask fill
47
+ `mask_area / bbox_area >= 0.30`.
48
+
49
+ For Lizard, the instance mask must also have at least 90% of its pixels in its
50
+ largest 8-connected component. This removes source instance IDs that span
51
+ separated regions and otherwise create huge background-filled boxes. The source
52
+ mask area is checked before this decision. NuCLS source rectangles/polygons do
53
+ not contain a mask area; no artificial mask-fill value is inferred for them.
54
+
55
+ RefPath and BCSS region targets require:
56
+
57
+ - short side at least 64 pixels;
58
+ - bbox area at least 4,096 pixels squared;
59
+ - bbox aspect ratio at most 6:1;
60
+ - bbox area no more than 80% of the parent area;
61
+ - target short side at least 8 pixels under the reference resize.
62
+
63
+ BCSS targets remain semantic connected regions, not cell instances. RefPath
64
+ descriptions remain unchanged.
65
+
66
+ MIDOG++ boxes are source-centered 50-pixel detection windows, not tight cell
67
+ contours. They are not subjected to the nucleus mask-fill test. Each retained
68
+ MIDOG++ record gets a deterministic local parent crop of at most 1024 x 1024
69
+ pixels that fully contains the source window and prefers a 64-pixel margin when
70
+ possible. The bbox and all coordinates are translated into that local parent;
71
+ the original parent path, dimensions and bbox are retained in `source`.
72
+ The crop grid has 512-pixel stride, includes the final image edge and does not
73
+ pad or resize the source pixels. A SHA-256 hash of seed, record ID and a fixed
74
+ operation name chooses among containing crops (preferably with the margin).
75
+ This is a label-aware training crop, not a claim of label-free inference.
76
+ Targets fully contained in the crop have translated boxes. Targets intersecting
77
+ but not fully contained are clipped only for the ignore region, marked
78
+ `partial_after_parent_crop`, and never positive targets. Completely outside
79
+ targets are not visible and do not appear in the local annotation.
80
+
81
+ The reference visibility check uses
82
+ `min(child_width, child_height) * min(1, 1024 / max(parent_width, parent_height))`.
83
+ It does not credit artificial enlargement with new detail. The actual training
84
+ processor can differ; this reference rule is a reproducible quality screen, not
85
+ a substitute for checking eventual model preprocessing. Lizard parents are not
86
+ recropped in this version. RefPath parents are never recropped because their
87
+ source expressions can contain original-frame spatial relations.
88
+
89
+ ## Multi-target semantics
90
+
91
+ Quality filtering is applied to every source target in each parent annotation,
92
+ not only to the selected manifest row. A row is retained only if its selected
93
+ target is eligible. The output annotation preserves every source-supported
94
+ target with:
95
+
96
+ - `training_eligible: true` for positive training supervision;
97
+ - `training_eligible: false` and `quality_filter_reasons` for an excluded target.
98
+
99
+ Excluded matching targets are **not negatives**. For every retained row,
100
+ `target_count` counts only eligible matching targets in the current view and
101
+ `ignored_target_count` records matching targets excluded by this filter.
102
+ `source_target_count` preserves the original row's matching-answer count, which
103
+ can include out-of-view targets for a MIDOG++ local crop.
104
+ Training code should use eligible matches for the positive answer set and should
105
+ not convert ignored matches into background/no-target examples.
106
+
107
+ All unrecropped annotations preserve every source target, including excluded
108
+ ones. Local MIDOG++ annotations preserve every intersecting target as described
109
+ above. Filtering never merges instance IDs, classes or source expressions, and
110
+ never converts excluded rows into negative examples.
111
+
112
+ ## Dataset-specific policy
113
+
114
+ The source order and labels are unchanged. Lizard is filtered more strictly
115
+ because its source instance maps contain a small number of disconnected IDs,
116
+ and because a large fraction of its nuclear boxes are below a useful input
117
+ resolution. MIDOG++ is made more usable by local-parent recropping rather than
118
+ by pretending that a 6,000 x 5,000 parent should be directly resized to a model
119
+ input. No extra positive samples are invented by recropping.
120
+
121
+ SegPath remains excluded because its existing manifest has a malformed NUL line
122
+ and its source summary disagrees with the materialized rows. PathBind grounding
123
+ remains excluded because its available examples are official testA/testB data.
124
+ The legacy PathGen16M caption subset remains outside this spatial grounding
125
+ training package.
126
+
127
+ ## Reproducibility and audit files
128
+
129
+ `quality_filter_policy.json` is the machine-readable policy. The output stores:
130
+
131
+ - `quality_filter_report.json`: counts, retention, overlapping exclusion reasons,
132
+ per-class counts, source hashes and provenance;
133
+ - `audit/row_decisions.jsonl`: one decision and metrics per source row;
134
+ - `review/samples.json`: seeded samples for later manual review, explicitly not
135
+ claimed to have been reviewed;
136
+ - `review/low_focus_candidates.json`: deterministic blur-review candidates;
137
+ - `FILTER_RESULTS.md`: compact count table;
138
+ - `metadata/unfiltered_export_index.json` and
139
+ `metadata/unfiltered_export_validation.json`: input release records;
140
+ - `METHODS.md`: this protocol.
141
+
142
+ The implementation records its own SHA-256. The source manifest is checked both
143
+ before and after filtering. All source parent decoded hashes are verified, and
144
+ every retained source child is fully compared in RGB against its source parent
145
+ crop. MIDOG++ parents are exact source crops without interpolation; children
146
+ are unchanged. Lizard MAT-file SHA-256 values are recorded and rechecked by the
147
+ lineage audit. Run both validators:
148
+
149
+ ```bash
150
+ python scripts/filter_auxiliary_training_quality.py \
151
+ --source /path/to/auxiliary_training_v2/zoompath_training_v4 \
152
+ --original-root /path/to/auxiliary_training_v2 \
153
+ --output /path/to/auxiliary_training_v2/zoompath_training_v4_quality_filtered
154
+ python scripts/validate_auxiliary_training_export.py \
155
+ /path/to/auxiliary_training_v2/zoompath_training_v4_quality_filtered
156
+ python scripts/validate_auxiliary_quality_filter.py \
157
+ --source /path/to/auxiliary_training_v2/zoompath_training_v4 \
158
+ --original-root /path/to/auxiliary_training_v2 \
159
+ --output /path/to/auxiliary_training_v2/zoompath_training_v4_quality_filtered
160
+ ```
161
+
162
+ The output directory must not exist. The original source package remains
163
+ unchanged. Retained source images are hard-linked by default; `--copy` creates
164
+ independent copies instead. Do not modify any hard-linked images in place.
165
+ No train/val/test split is invented: original splits/folds and source groups
166
+ are retained, and any future split must keep a source group together, including
167
+ all of its MIDOG++ views.
168
+
169
+ Storage compaction is separate from the quality policy: it only removes
170
+ unfiltered local image names and backup-verified local caches after both
171
+ validators pass. Every backup path and size is checked; metadata and sampled
172
+ images receive SHA-256 comparison. Missing or differing backup directories
173
+ are preserved, not treated as safely deletable. The storage audit records the
174
+ canonical package, backup location, deleted/preserved directories and old export
175
+ metadata location. Deleting one hard link does not alter the retained image.
176
+ Historical input validation is archived evidence, not a claim that the archived
177
+ metadata-only directory still contains its original images.
178
+
179
+ For later manual inspection, six retained and six excluded rows per dataset
180
+ are selected by reservoir sampling with seed 20261010 (where available). The
181
+ contact sheets show both parent context with a target box and an enlarged
182
+ target preview. Enlargement is display-only. The lower 1% thumbnail focus
183
+ scores per dataset are exported as review candidates. Neither operation claims
184
+ that a person reviewed the samples. The optional `sampling_config.json`
185
+ recommends dataset/parent/query-aware sampling with Lizard probability 0.05;
186
+ these sampling weights are not applied by the export, and no training run is
187
+ claimed here.
188
+
189
+ ## Paper Methods Template
190
+
191
+ We constructed an auxiliary single-step pathology grounding training set from
192
+ seven source collections. Quality screening was deterministic and used separate
193
+ rules for nuclear-instance and tissue-region annotations. We excluded extremely
194
+ small or elongated parent images and targets, boundary-truncated nuclear
195
+ instances, low-fill instance boxes when segmentation masks were available,
196
+ and Lizard instances with less than 90% of their mask pixels in the largest
197
+ 8-connected component. We additionally required an 8-pixel target short side
198
+ under a reference long-edge-1024 downscale. MIDOG++ source detection windows
199
+ were retained in exact-pixel 1024-pixel local parent crops with translated
200
+ annotations; original-frame RefPath expressions were not recropped. Excluded
201
+ matching targets were preserved as ignored alternatives rather than negative
202
+ examples. Source questions, available organ/scale metadata, original split
203
+ identifiers and licensing provenance were retained. The initial set contained
204
+ 618,432 pairs; report the retained count from FILTER_RESULTS.md, along with
205
+ per-source retention and the implemented threshold table. Screening and pixel
206
+ consistency checks do not imply pathologist review; any subsequent manual
207
+ review must be described separately.
208
+
209
+ ## Limitations
210
+
211
+ The thresholds are engineering thresholds for auxiliary training, not
212
+ pathologist-defined inclusion criteria. They do not establish that every
213
+ retained crop is diagnostically useful, that every source annotation is correct,
214
+ or that the source collections are patient-independent. A later paper should
215
+ report the policy ID, source and output counts, class counts, exclusion reasons,
216
+ and whether the seeded review samples were manually inspected.
package/README.md CHANGED
@@ -1,217 +1,93 @@
1
- # ZoomPath Auxiliary Training v4
2
-
3
- This is a portable **single-step parent-child grounding training package**.
4
- It references the v4 benchmark's image/box/query conventions, but is not a
5
- Z1-Z12 benchmark, a WSI trajectory dataset, or a raw WSI release.
6
- The v4 benchmark's reported doctor review does not apply to this auxiliary package.
7
-
8
- ## Contents
9
-
10
- | Path | Contents |
11
- | --- | --- |
12
- | `manifest.jsonl` | One JSON object per selected source target/crop |
13
- | `index.json` | Dataset counts, metadata coverage, manifest checksum |
14
- | `images/parent/` | Input images, stored once per decoded pixel identity |
15
- | `images/child/` | Lossless target crops, without resizing |
16
- | `annotations/<dataset>/` | All source targets for each input parent |
17
- | `training_data.py` | Streaming reader and image/valid-target loader |
18
- | `validation.json` | Export audit with explicit pixel-check scope |
19
- | `metadata/` | Original source metadata, licenses, index and validation reports |
20
-
21
- All image and annotation paths in `manifest.jsonl` are **relative to this directory**.
22
- Historical absolute paths in `metadata/` and the `source_*` provenance fields of
23
- `index.json` are not used to load training data. Transfer this entire directory
24
- to another machine; no source image directory or original absolute path is required.
25
-
26
- ## Included Data
27
-
28
- | Source | Parent-child pairs |
29
- | --- | ---: |
30
- | RefPath train | 28,908 |
31
- | PanNuke | 187,831 |
32
- | NuInsSeg | 25,718 |
33
- | NuCLS | 35,848 |
34
- | BCSS | 6,153 |
35
- | Lizard | 325,966 |
36
- | MIDOGpp | 8,008 |
37
- | Total | 618,432 |
38
-
39
- Only the seven sources in the existing pixel-validated `training_index.json`
40
- are included. SegPath is excluded because its source manifest fails validation
41
- and its current row count disagrees with its generation summary. PathBind
42
- official testA/testB examples are not repurposed for training. PathGen16M is
43
- outside this spatial grounding build. No original files were removed or repaired.
44
-
45
- `partition` is `training`. `source_split` preserves the upstream split/fold.
46
- No artificial train/val/test split is invented. PanNuke and Lizard
47
- folds and NuInsSeg's unspecified split remain explicitly recorded. Keep all
48
- targets and questions for the same parent together in any later split;
49
- `source.source_group_id` and `source.source_group_field` carry the available grouping.
50
- The original overlap audit is copied, not a new claim of patient-level independence.
51
-
52
- ## Fields And Coordinates
53
-
54
- - `question`: Original training question. `query` is an alias.
55
- - `expression`: Original source description or class label. Do not rewrite it
56
- when applying `valid_targets_filter`.
57
- - `parent_image` and `child_image`: Relative path, `[width, height]`, and optional MPP/level.
58
- - `child_bbox_in_parent`: Integer `x/y/w/h` in the original input parent pixels.
59
- - `child_bbox_xyxy_in_parent`: `[x1, y1, x2, y2]`, top-left origin, half-open edges.
60
- The child equals `parent.crop((x1, y1, x2, y2))` in decoded RGB pixels.
61
- - `organ`: Normalized source organ/anatomical tissue site, or `null` if unsupported.
62
- NuInsSeg muscle/adipose/bone sites remain explicit tissue/site names.
63
- A generic tumor diagnosis or melanoma label alone is not converted to an organ.
64
- - `organ_source`: Provenance of organ/site metadata.
65
- - `parent_image.mpp`: `[microns_per_pixel_x, microns_per_pixel_y]` or `null`.
66
- BCSS uses its source filename's resampling MPP. MIDOGpp uses TIFF resolution
67
- tags only when both axes and physical units are present; these values are
68
- preserved. When only magnification is available, use the nominal training
69
- mapping below. The unresized child has the same MPP as its parent.
70
- - `parent_image.mpp_is_approximate`: `true` for nominal MPP assigned by the
71
- magnification mapping, `false` for retained source MPP (or no MPP).
72
- - `magnification`: Source or estimated equivalent training magnification;
73
- also stored on each image with its source and approximation flag.
74
- - `parent_image.level`: Training scale category `high`, `mid`, `low`, or `null`.
75
- **This is not a WSI pyramid index.** `source_level` is a separate field for
76
- a source pyramid index if one was explicitly available.
77
- - `annotation_kind`: Source annotation semantics. BCSS boxes enclose semantic
78
- connected regions, not cell instances. MIDOGpp boxes are source detection
79
- windows, not tight cell contours.
80
- - `license`, `license_source`, `source`, `source_extra`: Upstream provenance and restrictions.
81
-
82
- ## Nominal Training Scales
83
-
84
- These approximate conventions are for optional auxiliary training, not scanner
85
- calibration or precision benchmark measurements. Nominal MPP is `10 / magnification`.
86
-
87
- | Magnification | Nominal MPP | Training level |
88
- | --- | ---: | --- |
89
- | 40x | 0.25 | high |
90
- | 20x | 0.50 | high |
91
- | 10x | 1.0 | mid |
92
- | 5x | 2.0 | mid |
93
- | 2.5x | 4.0 | low |
94
- | 1x | 10.0 | low |
95
-
96
- For RefPath train, assign a **heuristic** magnification from the original
97
- parent image's longest side: 1024 pixels -> 40x, 2048 pixels -> 20x.
98
- This follows the released testA/testB size pattern but is not a verified
99
- per-image train magnification annotation. Both magnification and nominal MPP
100
- are explicitly flagged approximate; all these records are `high`.
101
- Do not infer scale from the selected child's smaller crop size.
102
-
103
- For retained BCSS/MIDOGpp source MPP, select the nearest nominal MPP in log space
104
- to obtain an equivalent magnification and training level, without rounding or
105
- overwriting the physical MPP. Other records with no scale evidence remain `null`.
106
- The mapping and coverage counts are recorded in `index.json`.
107
-
108
- ## Multiple Valid Answers
109
-
110
- An `annotations_path` file contains **all targets in the parent**, not just those
111
- matching this query. Apply `valid_targets_filter` (an exact `label` or `expression`
112
- match) to select the valid alternatives. Their number equals `target_count`.
113
- The row's selected crop/box is one valid answer, not necessarily the only one.
114
- Do not treat unselected matching targets as negatives. Identical image/question
115
- inputs can have different selected crops; these are legitimate alternative
116
- targets. A multi-target trainer should obtain all boxes through `valid_targets()`.
117
- For one-target sampling, sample from that same matching set.
118
-
119
- The manifest has one row per selected source target. Large target sets consequently
120
- produce more rows; row-uniform sampling is not parent/query-uniform sampling.
121
- Multiple descriptions may refer to the same box. Preserve description semantics
122
- and source labels rather than merging unrelated questions.
123
 
124
- ## Loading
 
 
 
 
125
 
126
- Pillow is required only for loading images. The manifest and target reader use
127
- the Python standard library. Put the package directory on `PYTHONPATH`, for example:
 
128
 
129
- ```bash
130
- export PYTHONPATH=/data/zoompath_training_v4:$PYTHONPATH
131
- ```
132
-
133
- ```python
134
- from training_data import AuxiliaryTrainingReader
135
-
136
- reader = AuxiliaryTrainingReader('/data/zoompath_training_v4')
137
- row = next(reader.iter_records(dataset='pannuke'))
138
- sample = reader.load_pair(row)
139
- parent = sample['parent_image'] # PIL RGB image
140
- child = sample['child_image']
141
- question = sample['question']
142
- selected_box = sample['child_bbox_xyxy_in_parent']
143
- all_valid_boxes = sample['valid_target_bboxes_xyxy_in_parent']
144
- organ, mpp, level = sample['organ'], sample['mpp'], sample['level']
145
- ```
146
 
147
- Stream `manifest.jsonl` rather than loading the entire file into memory.
148
- If images are resized or cropped by training transforms, transform every target
149
- box consistently. If image sizes change, source MPP must also be adjusted.
 
150
 
151
- ## Storage And Restrictions
 
 
 
152
 
153
- The local export uses **hard links**, not symlinks. It does not duplicate image
154
- data blocks and can be transferred independently. Do not edit image files in
155
- place: local hard links share their contents with the original source files.
156
- Metadata/annotations are independent copies. `--copy` creates independent image
157
- files when exporting to another filesystem.
158
-
159
- This package does not grant new redistribution rights. Source restrictions are
160
- retained per row and in `metadata/<dataset>/source.json`. RefPath is gated and
161
- is not assumed redistributable. The Hugging Face copy is intended for the private
162
- repository `xuxuxuxuxu/PathGrounding`; do not make it public or mirror it into a
163
- public dataset without checking every upstream license.
164
-
165
- ## Download From Hugging Face
166
-
167
- The repository is private, so authenticate with an account that has access:
168
 
169
  ```bash
170
- pip install -U huggingface_hub
171
- hf auth login
172
- hf download xuxuxuxuxu/PathGrounding \
173
- --repo-type dataset \
174
- --local-dir /data/pathgrounding-download
175
- python /data/pathgrounding-download/tools/restore_package.py \
176
- /data/pathgrounding-download /data/zoompath_training_v4
177
  ```
178
 
179
- Or download from Python and then use the package-relative paths in the manifest:
180
-
181
  ```python
182
- from huggingface_hub import snapshot_download
183
 
184
- root = snapshot_download(
185
- repo_id="xuxuxuxuxu/PathGrounding",
186
- repo_type="dataset",
187
- local_dir="/data/pathgrounding-download",
188
- )
 
189
  ```
190
 
191
- The repository preserves the complete package. Images and annotations are in
192
- uncompressed TAR shards to avoid uploading roughly 686,000 individual files;
193
- manifest, reader and metadata are in `package/`. The restore command verifies
194
- every shard's SHA-256 checksum and restores the original relative paths without
195
- resizing or changing any image, annotation or question. It requires Python 3.10+.
 
 
 
 
 
 
 
196
 
197
- The full package is about 54 GB. Keep roughly 115 GB of free disk space when
198
- retaining both the download and restored package. `snapshot_download` can resume
199
- an interrupted transfer. The full export validator mentioned below belongs to
200
- the ZoomPath code repository, not the downloadable data package.
201
 
202
- ## Rebuild And Validate
203
 
204
- From the ZoomPath code repository, using its existing environment:
 
205
 
206
  ```bash
207
- python scripts/export_auxiliary_training_v4.py --root /path/to/auxiliary_training_v2 --output /path/to/new-export
208
- python scripts/validate_auxiliary_training_export.py /path/to/new-export
 
 
 
209
  ```
210
 
211
- The exporter verifies that source manifests still match their recorded pixel
212
- validation checksums and retains all targets in each parent annotation file.
213
- The export validator checks every row's paths, box bounds, query target coverage,
214
- selected-target linkage and metadata consistency. By default it checks crop
215
- pixels and decoded parent hashes on 100 deterministic random samples per dataset.
216
- The report separates that sample check from the upstream full pixel validation.
217
- Use `--all-pixels` for a full export pixel check.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # ZoomPath Auxiliary Training v4: Quality-Filtered Derivative
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2
 
3
+ This package is a deterministic, automatically quality-filtered derivative of
4
+ the validated ZoomPath auxiliary parent-child grounding export. It is intended
5
+ for additional training, not as a clinically reviewed benchmark. A local
6
+ storage compaction may replace the working unfiltered export with this package;
7
+ the complete original collection remains in the separately verified backup.
8
 
9
+ Read `METHODS.md` before using the data. The exact machine-readable thresholds
10
+ are in `quality_filter_policy.json`; source-row decisions are in
11
+ `audit/row_decisions.jsonl`; aggregate counts are in `quality_filter_report.json`.
12
 
13
+ ## Important semantics
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
14
 
15
+ The package keeps source-supported alternative answers in each annotation file.
16
+ `training_eligible: true` targets are the positive training answers. Targets
17
+ with `training_eligible: false` were excluded for quality reasons and are **not
18
+ negative examples**. Use `target_count` and the reader's valid-target helper.
19
 
20
+ The questions, source descriptions, organ names, MPP/level metadata and source
21
+ provenance are retained. MIDOG++ has deterministic local parent crops no larger
22
+ than 1024 x 1024; the original parent geometry is preserved in each row's
23
+ `source` field.
24
 
25
+ ## Loading
 
 
 
 
 
 
 
 
 
 
 
 
 
 
26
 
27
  ```bash
28
+ export PYTHONPATH=/path/to/zoompath_training_v4:$PYTHONPATH
 
 
 
 
 
 
29
  ```
30
 
 
 
31
  ```python
32
+ from training_data import AuxiliaryTrainingReader
33
 
34
+ reader = AuxiliaryTrainingReader("/path/to/zoompath_training_v4")
35
+ row = next(reader.iter_records(dataset="pannuke"))
36
+ sample = reader.load_pair(row)
37
+ positive_boxes = sample["valid_target_bboxes_xyxy_in_parent"]
38
+ ignored_boxes = sample["ignored_target_bboxes_xyxy_in_parent"]
39
+ assert sample["ignored_targets_are_not_negatives"]
40
  ```
41
 
42
+ Boxes use source-native integer pixel XYXY with half-open edges. Stream the
43
+ manifest; do not load every row into RAM. Do not use a legacy reader that treats
44
+ all matching targets as eligible. Ignored boxes should not create negative
45
+ labels or penalties in the training loss. If the training system cannot support
46
+ ignore regions, use a compatible objective or exclude affected parent/query
47
+ inputs rather than silently relabeling their ignored targets as background.
48
+
49
+ `sampling_config.json` contains optional dataset mixture probabilities (Lizard
50
+ 0.05) and parent/query-aware sampling guidance; no sampler is applied by the
51
+ export. Do not split different local crops from the same MIDOG++ source image
52
+ across training and validation sets. Source folds and grouping fields remain
53
+ available; no new train/val/test partition is created by this filter.
54
 
55
+ The output uses hard links for unchanged source images and new PNG files for
56
+ MIDOG++ local parents. This is independently transferable, but local hard links
57
+ share storage blocks with the original export: do not edit them in place.
58
+ Unlinking an old unfiltered image does not damage its retained hard link.
59
 
60
+ ## Validation
61
 
62
+ The package is generated separately from the original export and should be
63
+ validated with the repository's export validator before training:
64
 
65
  ```bash
66
+ python scripts/validate_auxiliary_training_export.py /path/to/zoompath_training_v4_quality_filtered
67
+ python scripts/validate_auxiliary_quality_filter.py \
68
+ --source /path/to/auxiliary_training_v2/zoompath_training_v4 \
69
+ --original-root /path/to/auxiliary_training_v2 \
70
+ --output /path/to/zoompath_training_v4_quality_filtered
71
  ```
72
 
73
+ The package is automatically filtered and has not received doctor/pathologist
74
+ manual review. The `review/` directory contains deterministic candidates for a
75
+ later audit only.
76
+
77
+ ## Local storage compaction
78
+
79
+ The canonical local training package is `auxiliary_training_v2/zoompath_training_v4`.
80
+ Root-level `training_index.json` and `training_manifest.jsonl` are convenience
81
+ links to this filtered package, not the old unfiltered absolute-path schema.
82
+ Only this package is a training entry point; any preserved source/cache
83
+ directories are not additional eligible training records.
84
+
85
+ `storage_compaction.json` records backup inventory checks, deleted directories
86
+ and directories preserved because the backup was missing or different. Backup
87
+ verification checks every relative path and file size plus metadata and sampled
88
+ image hashes; it does not claim exhaustive source-image byte comparison.
89
+ The old export's manifest, annotations and validation history remain in the
90
+ external audit directory without its image tree, for later lineage checks.
91
+ After compaction, pass that metadata directory as `--source` and the original
92
+ backup root as `--original-root` to `validate_auxiliary_quality_filter.py`.
93
+ The storage operation does not update the Hugging Face release.
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4
  "benchmark_inclusion": false,
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14
  "manual_quality_review": "not_claimed_for_auxiliary_training",
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  "dataset_counts": {
16
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87
  "nominal_mpp_formula": "10 / magnification; not scanner-calibrated",
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144
  "segpath": "not in training_index ready datasets; current source validation is not a pass",
145
  "pathbind_grounding": "official testA/testB evaluation data",
146
  "pathgen16m": "outside spatial auxiliary training build"
147
- }
 
 
 
 
 
148
  }
 
2
  "schema_version": "zoompath.auxiliary_training.export.v1",
3
  "data_role": "auxiliary_training",
4
  "benchmark_inclusion": false,
5
+ "source_root": "/jhcnas3/jiahao/ZoomPath/auxiliary_training_v2",
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+ "source_manifest": "/jhcnas3/jiahao/ZoomPath/auxiliary_training_v2/training_manifest.jsonl",
7
  "source_manifest_sha256": "d3a6324a1a53a6c95432a288591830008b3dfe4eea7342bdf8d037299fc79706",
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  "manifest": "manifest.jsonl",
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24
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64
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  "nominal_mpp_formula": "10 / magnification; not scanner-calibrated",
 
144
  "segpath": "not in training_index ready datasets; current source validation is not a pass",
145
  "pathbind_grounding": "official testA/testB evaluation data",
146
  "pathgen16m": "outside spatial auxiliary training build"
147
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148
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152
+ "source_export_metadata_root": "/storage/ssd1/jiahao/ZoomPath/audit/auxiliary_quality_filter_20261010/unfiltered_export_metadata"
153
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137
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138
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141
+ "path_policy": "all manifest image and annotation paths are relative to this directory",
142
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143
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144
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147
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148
+ }
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+ "content_hashes": {
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+ "lizard.zip.download.json": "d85594c1800a0e7e6785b54a3548312ece4a8baa9ff7314076fe36c37df6fa73",
207
+ "source.json": "938c75b75081de8743ba611cc30fd9c7c2c01360b522ab40898140eedb9d83f9",
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+ "data/lizard_images1/Lizard_Images1/consep_1.png": "b4857224beed54514bd33d0fe4edb9d43c1a1d14a8f7c2cd401c7f07d8791554",
209
+ "data/lizard_images1/Lizard_Images1/consep_10.png": "e767ee8144ae9a6fbf0cc58a35c8a99109aa4b4583acbb73186d65742357e7fd",
210
+ "data/lizard_images1/Lizard_Images1/consep_11.png": "d34c8d36933d7e9d95bbd705278dcf7e6f5a2694065b6f8a0e6aa1116ce00862",
211
+ "data/lizard_images1/Lizard_Images1/consep_12.png": "01abb2bfac3c543643502254eb403acf88590926fcde18035dfb9f93cc5e8241"
212
+ },
213
+ "verification": "all relative file paths and sizes; top-level metadata and sampled PNG SHA-256"
214
+ },
215
+ {
216
+ "path": "sources/midogpp",
217
+ "backup_verified": true,
218
+ "content_hashes": {
219
+ "annotations.json": "8c87ab4276eb11f6b6b5ac0d956b89dad7568a5474e0904b653d54dc68091f47",
220
+ "annotations.json.download.json": "0112c795e0c15c244f05ea4668b6c2fe8dc8c6c1d68ff2ccbf2b3a207265770b",
221
+ "source.json": "d2db14fcaa20d10071984de750721509007df84d94d28eaa56cd645a4de1387b",
222
+ "splits.csv.download.json": "ff0d1945883182f18f8a619731f6f8502fb486194332accf83580da6dfbd2a69"
223
+ },
224
+ "verification": "all relative file paths and sizes; top-level metadata and sampled PNG SHA-256"
225
+ },
226
+ {
227
+ "path": "pathbind_grounding",
228
+ "backup_verified": true,
229
+ "content_hashes": {
230
+ "EXCLUDED_FROM_TRAINING.md": "46ad0839b9317508fb1c8a151cdcd5b008b6076fbe4971c8d45e9d2280f16c4e",
231
+ "SOURCE_NOTICE.md": "5aed2e74d881d7b9fe4bea71dcb0c9cdd33e52bc9b85ad5b96fe78a9b6f10667",
232
+ "manifest.jsonl": "6d5089b7e7b5b9280dff0850914f497a5975945606ca5c327e757de988072811",
233
+ "summary.json": "57bc670cfb32f9bd724744167d51d5e8df868f5f62b3446fea4924ef90adf877"
234
+ },
235
+ "verification": "all relative file paths and sizes; top-level metadata and sampled PNG SHA-256"
236
+ },
237
+ {
238
+ "path": "pathbind_source",
239
+ "backup_verified": true,
240
+ "content_hashes": {
241
+ "README.md": "9b370accf95e7c67746b14936890b3e755c444a92d2ce783cc171bde5dc49392"
242
+ },
243
+ "verification": "all relative file paths and sizes; top-level metadata and sampled PNG SHA-256"
244
+ },
245
+ {
246
+ "path": "pathgen16m",
247
+ "backup_verified": true,
248
+ "content_hashes": {
249
+ "SOURCE_NOTICE.md": "9259fdd755b5096c025fdfff583f655c3b39c25818cf80b3656a72c4e4fa4eeb",
250
+ "manifest.jsonl": "f57b5d894f8d5d0aa4458de17b5a6ff2349f09d1fc88e171ddddc91b03a40e04",
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+ "summary.json": "dc6295d7d4035afae6320746968d9cc4927a6dde7f0f3d2d8789ba2a80bd0835"
252
+ },
253
+ "verification": "all relative file paths and sizes; top-level metadata and sampled PNG SHA-256"
254
+ },
255
+ {
256
+ "path": "refpath_source",
257
+ "backup_verified": true,
258
+ "content_hashes": {
259
+ "README.md": "9ff61105c61254e916ceea30a16930d1d735ca39d09a85b3e0bab5d7cbc84676",
260
+ "testA.jsonl": "0b60532f01864138f8e842af7f4899ec2d89daa1d53fd5299055cf1a504a4ebd",
261
+ "testB.jsonl": "b5cb0103aece56bc711098d281133f8f915615614b517b9431ffce9a86691909"
262
+ },
263
+ "verification": "all relative file paths and sizes; top-level metadata and sampled PNG SHA-256"
264
+ },
265
+ {
266
+ "path": "segpath",
267
+ "backup_verified": false,
268
+ "reason": "missing_or_linked_directory"
269
+ },
270
+ {
271
+ "path": "sources/segpath",
272
+ "backup_verified": false,
273
+ "reason": "backup_inventory_differs",
274
+ "difference_count": 317407,
275
+ "difference_examples": [
276
+ ">f+++++++++ CD235a_RBC.tar.gz",
277
+ ">f+++++++++ CD235a_RBC.tar.gz.download.json",
278
+ ">f+++++++++ CD3CD20_Lymphocyte.tar.gz",
279
+ ">f+++++++++ CD3CD20_Lymphocyte.tar.gz.download.json",
280
+ ">f+++++++++ CD45RB_Leukocyte.tar.gz",
281
+ ">f+++++++++ CD45RB_Leukocyte.tar.gz.download.json",
282
+ ">f+++++++++ ERG_Endothelium.tar.gz",
283
+ ">f+++++++++ ERG_Endothelium.tar.gz.download.json",
284
+ ">f+++++++++ MIST1_PlasmaCell.tar.gz",
285
+ ">f+++++++++ MIST1_PlasmaCell.tar.gz.download.json"
286
+ ]
287
+ }
288
+ ],
289
+ "delete_directories": [
290
+ "refpath_train",
291
+ "pannuke",
292
+ "nuinsseg",
293
+ "nucls",
294
+ "bcss",
295
+ "lizard",
296
+ "midogpp",
297
+ "sources/refpath_train",
298
+ "sources/pannuke",
299
+ "sources/nuinsseg",
300
+ "sources/nucls",
301
+ "sources/bcss",
302
+ "sources/lizard",
303
+ "sources/midogpp",
304
+ "pathbind_grounding",
305
+ "pathbind_source",
306
+ "pathgen16m",
307
+ "refpath_source"
308
+ ],
309
+ "preserve_directories": [
310
+ "segpath",
311
+ "sources/segpath"
312
+ ],
313
+ "disk_free_before": 279731953664,
314
+ "unfiltered_metadata_archive": "/storage/ssd1/jiahao/ZoomPath/audit/auxiliary_quality_filter_20261010/unfiltered_export_metadata",
315
+ "manual_review_performed": false,
316
+ "huggingface_updated": false,
317
+ "status": "complete",
318
+ "operations": [
319
+ {
320
+ "action": "promote_filtered_and_archive_source_metadata"
321
+ },
322
+ {
323
+ "action": "delete_backup_verified_directory",
324
+ "path": "refpath_train"
325
+ },
326
+ {
327
+ "action": "delete_backup_verified_directory",
328
+ "path": "pannuke"
329
+ },
330
+ {
331
+ "action": "delete_backup_verified_directory",
332
+ "path": "nuinsseg"
333
+ },
334
+ {
335
+ "action": "delete_backup_verified_directory",
336
+ "path": "nucls"
337
+ },
338
+ {
339
+ "action": "delete_backup_verified_directory",
340
+ "path": "bcss"
341
+ },
342
+ {
343
+ "action": "delete_backup_verified_directory",
344
+ "path": "lizard"
345
+ },
346
+ {
347
+ "action": "delete_backup_verified_directory",
348
+ "path": "midogpp"
349
+ },
350
+ {
351
+ "action": "delete_backup_verified_directory",
352
+ "path": "sources/refpath_train"
353
+ },
354
+ {
355
+ "action": "delete_backup_verified_directory",
356
+ "path": "sources/pannuke"
357
+ },
358
+ {
359
+ "action": "delete_backup_verified_directory",
360
+ "path": "sources/nuinsseg"
361
+ },
362
+ {
363
+ "action": "delete_backup_verified_directory",
364
+ "path": "sources/nucls"
365
+ },
366
+ {
367
+ "action": "delete_backup_verified_directory",
368
+ "path": "sources/bcss"
369
+ },
370
+ {
371
+ "action": "delete_backup_verified_directory",
372
+ "path": "sources/lizard"
373
+ },
374
+ {
375
+ "action": "delete_backup_verified_directory",
376
+ "path": "sources/midogpp"
377
+ },
378
+ {
379
+ "action": "delete_backup_verified_directory",
380
+ "path": "pathbind_grounding"
381
+ },
382
+ {
383
+ "action": "delete_backup_verified_directory",
384
+ "path": "pathbind_source"
385
+ },
386
+ {
387
+ "action": "delete_backup_verified_directory",
388
+ "path": "pathgen16m"
389
+ },
390
+ {
391
+ "action": "delete_backup_verified_directory",
392
+ "path": "refpath_source"
393
+ }
394
+ ],
395
+ "completed_at": "2026-10-10T04:06:57+08:00",
396
+ "disk_free_after": 391760920576,
397
+ "filesystem_free_bytes_increase": 112028966912
398
+ }
package/training_data.py CHANGED
@@ -41,6 +41,7 @@ class AuxiliaryTrainingReader:
41
  self._annotation_path: str | None = None
42
  self._targets: list[dict[str, Any]] = []
43
  self._groups: dict[tuple[str, str], list[dict[str, Any]]] = {}
 
44
 
45
  def resolve(self, relative: str) -> Path:
46
  path = Path(relative)
@@ -62,10 +63,12 @@ class AuxiliaryTrainingReader:
62
  payload = json.loads(self.resolve(path).read_text(encoding="utf-8"))
63
  self._targets = payload["targets"]
64
  self._groups = {}
 
65
  for target in self._targets:
 
66
  for key in ("label", "expression"):
67
  if key in target:
68
- self._groups.setdefault((key, target[key]), []).append(target)
69
  self._annotation_path = path
70
  return self._targets
71
 
@@ -80,6 +83,12 @@ class AuxiliaryTrainingReader:
80
  raise ValueError(f"Target count mismatch: {row['record_id']}")
81
  return matches
82
 
 
 
 
 
 
 
83
  def load_pair(self, row: dict[str, Any]) -> dict[str, Any]:
84
  from PIL import Image
85
 
@@ -92,6 +101,8 @@ class AuxiliaryTrainingReader:
92
  "parent_image": parent, "child_image": child, "question": row["question"],
93
  "child_bbox_xyxy_in_parent": row["child_bbox_xyxy_in_parent"],
94
  "valid_target_bboxes_xyxy_in_parent": [target["bbox_xyxy"] for target in self.valid_targets(row)],
 
 
95
  "organ": row["organ"], "mpp": row["parent_image"]["mpp"],
96
  "magnification": row["parent_image"].get("magnification"),
97
  "mpp_is_approximate": row["parent_image"].get("mpp_is_approximate", False),
 
41
  self._annotation_path: str | None = None
42
  self._targets: list[dict[str, Any]] = []
43
  self._groups: dict[tuple[str, str], list[dict[str, Any]]] = {}
44
+ self._ignored_groups: dict[tuple[str, str], list[dict[str, Any]]] = {}
45
 
46
  def resolve(self, relative: str) -> Path:
47
  path = Path(relative)
 
63
  payload = json.loads(self.resolve(path).read_text(encoding="utf-8"))
64
  self._targets = payload["targets"]
65
  self._groups = {}
66
+ self._ignored_groups = {}
67
  for target in self._targets:
68
+ groups = self._groups if target.get("training_eligible", True) else self._ignored_groups
69
  for key in ("label", "expression"):
70
  if key in target:
71
+ groups.setdefault((key, target[key]), []).append(target)
72
  self._annotation_path = path
73
  return self._targets
74
 
 
83
  raise ValueError(f"Target count mismatch: {row['record_id']}")
84
  return matches
85
 
86
+ def ignored_targets(self, row: dict[str, Any]) -> list[dict[str, Any]]:
87
+ """Source-supported query matches excluded from positive training supervision."""
88
+ self.annotation_targets(row)
89
+ key, value = next(iter(row["valid_targets_filter"].items()))
90
+ return self._ignored_groups.get((key, value), [])
91
+
92
  def load_pair(self, row: dict[str, Any]) -> dict[str, Any]:
93
  from PIL import Image
94
 
 
101
  "parent_image": parent, "child_image": child, "question": row["question"],
102
  "child_bbox_xyxy_in_parent": row["child_bbox_xyxy_in_parent"],
103
  "valid_target_bboxes_xyxy_in_parent": [target["bbox_xyxy"] for target in self.valid_targets(row)],
104
+ "ignored_target_bboxes_xyxy_in_parent": [target["bbox_xyxy"] for target in self.ignored_targets(row)],
105
+ "ignored_targets_are_not_negatives": bool(row.get("quality_filter")),
106
  "organ": row["organ"], "mpp": row["parent_image"]["mpp"],
107
  "magnification": row["parent_image"].get("magnification"),
108
  "mpp_is_approximate": row["parent_image"].get("mpp_is_approximate", False),
package/validation.json CHANGED
@@ -2,81 +2,81 @@
2
  "schema_version": "zoompath.auxiliary_training.export.validation.v1",
3
  "status": "pass",
4
  "manifest": "manifest.jsonl",
5
- "manifest_sha256": "878b7e84d153a63006539477a26519d775a10358290c33406c69c1b80354c25c",
6
- "row_count": 618432,
7
  "dataset_counts": {
8
- "bcss": 6153,
9
- "lizard": 325966,
10
  "midogpp": 8008,
11
- "nucls": 35848,
12
- "nuinsseg": 25718,
13
- "pannuke": 187831,
14
- "refpath_train": 28908
15
  },
16
- "annotation_file_count": 34193,
17
- "unique_parent_count": 34193,
18
- "multi_valid_target_rows": 594683,
19
  "organ_counts": {
20
- "Adipose tissue": 466,
21
- "Adrenal gland": 6873,
22
- "Bile duct": 8960,
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- "Bladder": 3155,
24
- "Brain": 111,
25
- "Breast": 92592,
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- "Cerebellum": 421,
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- "Cervix": 7226,
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- "Colon": 35333,
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- "Colon and rectum": 325966,
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- "Epiglottis": 131,
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- "Esophagus": 10961,
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- "Kidney": 6565,
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- "Liver": 6378,
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- "Lung": 7864,
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- "Muscle": 214,
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- "Ovary": 3825,
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- "Pancreas": 6241,
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- "Peritoneum": 386,
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- "Placenta": 1632,
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- "Prostate": 4131,
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- "Rectum": 329,
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- "Salivary gland": 2672,
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- "Skin": 8832,
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- "Spleen": 4637,
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- "Stomach": 6301,
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- "Testis": 5022,
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- "Tongue": 1243,
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- "Tonsil": 930,
55
- "Umbilical cord": 77,
56
- "Uterus": 8686,
57
- "unknown": 34861
58
  },
59
  "mpp_counts": {
60
- "present": 43069,
61
- "missing": 575363
62
  },
63
  "level_counts": {
64
- "present": 43069,
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- "missing": 575363
66
  },
67
  "magnification_counts": {
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- "20.0": 22234,
69
- "40.0": 20835,
70
- "missing": 575363
71
  },
72
  "mpp_approximation_counts": {
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- "approximate": 28908,
74
- "missing": 575363,
75
- "source_metadata": 14161
76
  },
77
  "scale_level_counts": {
78
- "high": 43069,
79
- "missing": 575363
80
  },
81
  "validation_scope": "all rows: paths, unique IDs, bounds, query coverage, source target linkage and metadata; pixels per pixel_check_mode",
82
  "pixel_check_mode": "deterministic_sample",
 
2
  "schema_version": "zoompath.auxiliary_training.export.validation.v1",
3
  "status": "pass",
4
  "manifest": "manifest.jsonl",
5
+ "manifest_sha256": "eeda70abfec170771f9084d2330011dcfa8443c2132ea8a2e8a576296e78fa85",
6
+ "row_count": 235346,
7
  "dataset_counts": {
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9
+ "lizard": 31446,
10
  "midogpp": 8008,
11
+ "nucls": 32538,
12
+ "nuinsseg": 20521,
13
+ "pannuke": 111605,
14
+ "refpath_train": 28392
15
  },
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+ "annotation_file_count": 38836,
17
+ "unique_parent_count": 38836,
18
+ "multi_valid_target_rows": 209945,
19
  "organ_counts": {
20
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21
+ "Adrenal gland": 4506,
22
+ "Bile duct": 5516,
23
+ "Bladder": 1929,
24
+ "Brain": 78,
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55
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58
  },
59
  "mpp_counts": {
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+ "present": 39236,
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+ "missing": 196110
62
  },
63
  "level_counts": {
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  },
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+ "20.0": 18640,
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  },
72
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  },
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  "scale_level_counts": {
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+ "high": 39236,
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+ "missing": 196110
80
  },
81
  "validation_scope": "all rows: paths, unique IDs, bounds, query coverage, source target linkage and metadata; pixels per pixel_check_mode",
82
  "pixel_check_mode": "deterministic_sample",
release_manifest.json CHANGED
@@ -2,279 +2,189 @@
2
  "schema_version": "zoompath.auxiliary_training.archive.v1",
3
  "repo_id": "xuxuxuxuxu/PathGrounding",
4
  "status": "frozen_release",
5
- "created_at": "2026-10-10T00:53:07+08:00",
6
- "source_manifest_sha256": "878b7e84d153a63006539477a26519d775a10358290c33406c69c1b80354c25c",
7
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8
- "package_bytes": 54066861824,
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- "pair_count": 618432,
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- "unique_parent_count": 34193,
11
  "dataset_counts": {
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- "bcss": 6153,
13
- "lizard": 325966,
14
  "midogpp": 8008,
15
- "nucls": 35848,
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- "nuinsseg": 25718,
17
- "pannuke": 187831,
18
- "refpath_train": 28908
19
  },
20
  "bulk_counts": {
21
- "images/parent": 34193,
22
- "images/child": 618432,
23
- "annotations": 34193
24
  },
25
- "total_upload_bytes": 55292110389,
26
  "files": [
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  {
28
  "path": "archives/images-parent-00000.tar",
29
  "kind": "archive",
30
- "bytes": 2113556480,
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- "sha256": "e27b645bbf3f983395ead2d0524c45fb17f57fbcb36109502c5e0eec66c77644",
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- "unpacked_bytes": 2110243595
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36
  "path": "archives/images-parent-00001.tar",
37
  "kind": "archive",
38
- "bytes": 2018375680,
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- "sha256": "59cf7588f1d0371b544ccec94f9fe3372696acdabf6aa3a5e2c3713693816256",
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- "member_count": 1264,
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- "unpacked_bytes": 2016104508
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  {
44
  "path": "archives/images-parent-00002.tar",
45
  "kind": "archive",
46
- "bytes": 2032537600,
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- "sha256": "618d0e26ca2280d54d1df8b3a267eeedc8f21aed726440a5ee7a0ada17b52577",
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- "bytes": 2091427840,
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  "path": "archives/images-parent-00004.tar",
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