Datasets:

xuxuxuxuxu
/

PathGrounding

Languages:

English

Tags:

Dataset card Files Files and versions

xet

Community

Dataset Viewer

Duplicate

Split (1)

train

The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
status: string
row_count: int64
parent_image_count: int64
pixel_verified_count: int64
multi_valid_target_rows: int64
error_count: int64
errors: list<item: null>
  child 0, item: null
validation_scope: string
manifest: string
manifest_sha256: string
license: string
official_source: string
revision: string
images: struct<url: string, bytes: int64, sha256: string>
  child 0, url: string
  child 1, bytes: int64
  child 2, sha256: string
storage_mode: string
checked_at: timestamp[s]
dataset: string
heldout_annotations: struct<testA: struct<url: string, bytes: int64, sha256: string>, testB: struct<url: string, bytes: i (... 22 chars omitted)
  child 0, testA: struct<url: string, bytes: int64, sha256: string>
      child 0, url: string
      child 1, bytes: int64
      child 2, sha256: string
  child 1, testB: struct<url: string, bytes: int64, sha256: string>
      child 0, url: string
      child 1, bytes: int64
      child 2, sha256: string
csv_entry: null
annotations: struct<url: string, bytes: int64, sha256: string>
  child 0, url: string
  child 1, bytes: int64
  child 2, sha256: string
source_split: string
to
{'dataset': Value('string'), 'csv_entry': Value('null'), 'status': Value('string'), 'checked_at': Value('timestamp[s]'), 'storage_mode': Value('string'), 'revision': Value('string'), 'official_source': Value('string'), 'license': Value('string'), 'source_split': Value('string'), 'annotations': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}, 'heldout_annotations': {'testA': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}, 'testB': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}}, 'images': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              status: string
              row_count: int64
              parent_image_count: int64
              pixel_verified_count: int64
              multi_valid_target_rows: int64
              error_count: int64
              errors: list<item: null>
                child 0, item: null
              validation_scope: string
              manifest: string
              manifest_sha256: string
              license: string
              official_source: string
              revision: string
              images: struct<url: string, bytes: int64, sha256: string>
                child 0, url: string
                child 1, bytes: int64
                child 2, sha256: string
              storage_mode: string
              checked_at: timestamp[s]
              dataset: string
              heldout_annotations: struct<testA: struct<url: string, bytes: int64, sha256: string>, testB: struct<url: string, bytes: i (... 22 chars omitted)
                child 0, testA: struct<url: string, bytes: int64, sha256: string>
                    child 0, url: string
                    child 1, bytes: int64
                    child 2, sha256: string
                child 1, testB: struct<url: string, bytes: int64, sha256: string>
                    child 0, url: string
                    child 1, bytes: int64
                    child 2, sha256: string
              csv_entry: null
              annotations: struct<url: string, bytes: int64, sha256: string>
                child 0, url: string
                child 1, bytes: int64
                child 2, sha256: string
              source_split: string
              to
              {'dataset': Value('string'), 'csv_entry': Value('null'), 'status': Value('string'), 'checked_at': Value('timestamp[s]'), 'storage_mode': Value('string'), 'revision': Value('string'), 'official_source': Value('string'), 'license': Value('string'), 'source_split': Value('string'), 'annotations': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}, 'heldout_annotations': {'testA': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}, 'testB': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}}, 'images': {'url': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')}}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

PathGrounding

Public, portable auxiliary training data for single-step pathology grounding. The main package contains 235,346 quality-filtered parent-child pairs from seven sources, with 38,836 distinct parent images. It replaces the earlier unfiltered 618,432-pair main release.

SegPath is a separate, optional package under segpath/: 2,193,058 pairs and 105,864 parents. It is not merged into the main manifest, counts, reader or sampling configuration. It is disabled by default. Download it explicitly only when needed; its mostly small semantic-region boxes differ from the larger region-of-interest navigation targets in ZoomPath v4.

These are auxiliary training data, not the ZoomPath v4 benchmark. They contain no raw WSIs and have not inherited the benchmark's doctor/pathologist review. Upstream licenses and restrictions apply; RefPath is gated and redistribution permission is not assumed; users must follow all relevant upstream terms. SegPath is CC-BY-NC-SA 4.0.

Main Package

Source	Retained pairs
BCSS	2,836
Lizard	31,446
MIDOGpp	8,008
NuCLS	32,538
NuInsSeg	20,521
PanNuke	111,605
RefPath train	28,392
Main total, excluding SegPath	235,346

Each record includes parent/child images, an exact parent-relative child box, text question, organ and available scale metadata. partition is training; original source split/fold information is retained, with no new train/val/test split. Keep all related targets/parents, and source slides when identifiable, together when defining later splits.

Python 3.10+ is required. The repository is public; authentication is not required for downloads. The explicit allow-list below does not download SegPath:

pip install -U huggingface_hub pillow

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="xuxuxuxuxu/PathGrounding",
    repo_type="dataset",
    local_dir="/data/pathgrounding-download",
    allow_patterns=["README.md", "release_manifest.json", "package/**",
                    "archives/**", "tools/restore_package.py"],
)

python /data/pathgrounding-download/tools/restore_package.py \
  /data/pathgrounding-download /data/zoompath_training_v4
export PYTHONPATH=/data/zoompath_training_v4:$PYTHONPATH

from training_data import AuxiliaryTrainingReader

reader = AuxiliaryTrainingReader("/data/zoompath_training_v4")
row = next(reader.iter_records(dataset="refpath_train"))
sample = reader.load_pair(row)
parent, child = sample["parent_image"], sample["child_image"]
question = sample["question"]
selected_box = sample["child_bbox_xyxy_in_parent"]
valid_boxes = sample["valid_target_bboxes_xyxy_in_parent"]
ignored_boxes = sample["ignored_target_bboxes_xyxy_in_parent"]
organ, mpp, level = sample["organ"], sample["mpp"], sample["level"]

The main images and annotations are uncompressed TAR shards, approximately 2 GiB each. Restoration verifies SHA-256 and recreates relative paths without resizing or changing questions. It is resumable by rerunning the command. Use a dedicated output directory and allow space for both the downloaded shards and the restored package. Exact byte totals are in release_manifest.json.

Coordinates are integer pixel XYXY, top-left origin, half-open edges. Children are exact crops. For multi-target supervision, use all eligible valid_boxes, not every annotated box or only the selected one. Source-supported answers rejected by quality filtering remain ignored_boxes, never negatives. The reader checks the exact label/expression and target count. Stream JSONL; uniform row sampling is not uniform parent/query sampling.

level means the training category high/mid/low, not a WSI pyramid index. MPP is microns per pixel; nominal RefPath magnification/MPP are approximate and explicitly flagged. Unsupported metadata remains null.

Optional SegPath

Download only the independent SegPath package:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="xuxuxuxuxu/PathGrounding",
    repo_type="dataset",
    local_dir="/data/pathgrounding-download",
    allow_patterns=["segpath/**", "tools/verify_segpath_package.py"],
)

python /data/pathgrounding-download/tools/verify_segpath_package.py \
  /data/pathgrounding-download/segpath
export PYTHONPATH=/data/pathgrounding-download/segpath:$PYTHONPATH

Use AuxiliaryTrainingReader("/data/pathgrounding-download/segpath") in a separate reader/import context from the main package. The SegPath reader loads images and annotations directly from eight native, uncompressed marker TARs; no extraction is required. Reserve approximately 210 GiB plus download-cache headroom. segpath/release_manifest.json gives the exact total and checksums.

Only official training patches are included. Targets are 8-connected semantic mask regions, not cell instances or WSI navigation paths. Questions use full labels. Known organs are retained; unknown organs are null. All rows record 40x, approximate MPP [0.25, 0.25], and training level high.

SegPath's sampling_config.json sets enabled_by_default: false and default_mix_probability: 0.0. No combined manifest is provided. An explicitly chosen future experiment should cap its contribution and sample marker, then parent/query, then target, rather than concatenate rows and sample uniformly.

Layout And Validation

Repository path	Contents
`package/`	Main manifest, index, reader, quality policy, decisions, reports and provenance
`archives/`	Main parent/child images and per-parent annotations
`release_manifest.json`	Main-only inventory, counts, sizes and SHA-256 checksums
`tools/restore_package.py`	Main package checksum verification and restoration
`segpath/`	Independent optional SegPath manifest, index, TARs, reader and provenance
`segpath/release_manifest.json`	SegPath-only inventory, counts, sizes and checksums
`tools/verify_segpath_package.py`	SegPath checksums and sample loading check

Both packages report pass with zero errors in their own validation.json and quality_validation.json. The main export checks all 235,346 rows and 38,836 annotation views, with 700 deterministic crop-pixel samples. SegPath checks all 2,193,058 rows and 105,864 annotation views, with 1,000 deterministic crop-pixel rechecks; its source audit reconstructs every retained parent's original mask. These automated audits are not a claim of a new manual or physician review.

The main package preserves historical source metadata showing SegPath excluded from that build; this is intentional and does not invalidate the independent validated segpath/ release. Read each package's methods, quality policy, filter results and provenance before training. Pin a Hub commit revision for reproducible downloads.

Downloads last month: 1,123

Total file size:

252 GB