File size: 7,367 Bytes
736bf98 f415f91 f597f0f 736bf98 b44e99d 736bf98 71b300c 736bf98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | ---
license: cc-by-4.0
pretty_name: SlicePoint Hospitals
language: [en]
tags: [medical, ct, abdominal-aortic-aneurysm, federated-learning, non-iid, distribution-shift, dataset-shift, nifti]
size_categories: [n<1K]
configs:
- config_name: features
data_files:
- {split: hospital_a, path: hospital_a/features.parquet}
- {split: hospital_b, path: hospital_b/features.parquet}
- {split: hospital_c, path: hospital_c/features.parquet}
- {split: hospital_d, path: hospital_d/features.parquet}
- {split: hospital_e, path: hospital_e/features.parquet}
- {split: hospital_f, path: hospital_f/features.parquet}
- {split: hospital_g, path: hospital_g/features.parquet}
- config_name: labels
data_files:
- {split: hospital_a, path: hospital_a/labels.parquet}
- {split: hospital_b, path: hospital_b/labels.parquet}
- {split: hospital_c, path: hospital_c/labels.parquet}
- {split: hospital_d, path: hospital_d/labels.parquet}
- {split: hospital_e, path: hospital_e/labels.parquet}
- {split: hospital_f, path: hospital_f/labels.parquet}
- {split: hospital_g, path: hospital_g/labels.parquet}
- config_name: predictions
data_files:
- {split: hospital_a, path: hospital_a/predictions.parquet}
- {split: hospital_b, path: hospital_b/predictions.parquet}
- {split: hospital_c, path: hospital_c/predictions.parquet}
- {split: hospital_d, path: hospital_d/predictions.parquet}
- {split: hospital_e, path: hospital_e/predictions.parquet}
- {split: hospital_f, path: hospital_f/predictions.parquet}
- {split: hospital_g, path: hospital_g/predictions.parquet}
- config_name: embeddings
data_files:
- {split: hospital_a, path: hospital_a/embeddings.parquet}
- {split: hospital_b, path: hospital_b/embeddings.parquet}
- {split: hospital_c, path: hospital_c/embeddings.parquet}
- {split: hospital_d, path: hospital_d/embeddings.parquet}
- {split: hospital_e, path: hospital_e/embeddings.parquet}
- {split: hospital_f, path: hospital_f/embeddings.parquet}
- {split: hospital_g, path: hospital_g/embeddings.parquet}
- config_name: crosswalk
data_files: crosswalk.parquet
---
# SlicePoint Hospitals
Seven simulated hospitals for studying dataset shift and non-IID federated learning on
abdominal CT, with the answer keys. Each hospital is a sample of the
[SlicePoint AAA CT](https://huggingface.co/datasets/slicepoint/slicepoint-aaa-ct) scans,
and all but two have a problem planted on purpose: the kind of difference that makes an
AI model behave differently at a new site. They were built for the 2026 Nucleate
Pittsburgh hackathon, where teams had to find the problems. The game is over, so the keys
are published.
**Not for clinical use.** AAA labels come from a 30 mm diameter rule on the ground-truth
aorta masks that ship with TotalSegmentator (at `hospital_e`, a 25 mm lumen rule), not
from radiologist reads. Predictions apply the same rule to the public TotalSegmentator
model's masks; that model was trained on this dataset and has seen most of these scans, so
its results are not an independent test (see the SlicePoint AAA CT card;
`crosswalk.parquet` links each case to its scan there, and that card's `tables/cases.parquet` has the
`totalsegmentator_split`).
## The hospitals
| Hospital | Scans (patients) | AAA | What is different | How clearly it shows |
|---|---|---|---|---|
| `hospital_a` | 100 (100) | 9 | Nothing. A clean random sample; the reference the others are compared with | none |
| `hospital_b` | 80 (40 people under 60 IDs) | 7 | Patients scanned again and again, some under new IDs; 2 patients reappear at `hospital_e` | strong (repeats), weak (cross-site) |
| `hospital_c` | 80 (80) | 7 | Every scan thick-slice (5 mm) and venous phase: same patients, scanned differently | strong on acquisition, weak on Dice |
| `hospital_d` | 80 (80) | 20 | Far more aneurysms, and older, more male, more arterial phase, all moving together | strong; the factors are confounded |
| `hospital_e` | 80 (80) | 7 by its own rule | Labels measure the inside of the vessel and call AAA at 25 mm: same images, different truth | strong |
| `hospital_f` | 80 (80) | 7 | Nothing. Tests for false alarms | none |
| `hospital_g` | 80 (80) | 7 | The exam name gives away the answer (a shortcut), plus 2 `hospital_b` patients under new IDs | weak |
`keys/<hospital>.json` holds the full recipe of each hospital: the planted findings
(`waldos`: kind, severity, description, the evidence a correct finding cites), the
targets, what was realised, and one row per case (source scan, repeat or cross-site copy,
new patient id or not). `keys/practice/` has the reference measures given to teams for
`hospital_a` to `hospital_e`, with the chance levels two clean samples would show.
`crosswalk.parquet` maps every case to its scan in SlicePoint AAA CT
(`canonical_image_id`), its true patient, and whether its image was changed:
`hospital_c` ships 5 mm re-makes, and the repeat scans at `hospital_b` and `hospital_g`
have added noise and a few slices dropped, so no file is a byte copy of another.
## Layout
| Path | What |
|---|---|
| `hospital_<x>/images/<case_id>.nii.gz` | CT volume (1.5 mm; `hospital_c` 1.5 x 1.5 x 5 mm) |
| `hospital_<x>/masks/<case_id>_aorta.nii.gz` | Ground-truth aorta mask on the same grid |
| `hospital_<x>/{features,labels,predictions,embeddings}.parquet` | The SlicePoint input contract, joined on `case_id` |
| `keys/`, `crosswalk.parquet` | Answer keys, as above |
| `manifest.json` | The practice release manifest: row counts and sha256 of the `hospital_a` to `hospital_e` tables as first released (they are unchanged) |
Column meanings are the same as in SlicePoint AAA CT. Race and ethnicity are not
recorded in the source, so no hospital differs along those lines and none can be tested
for them. `study_description` and
`protocol_name` are synthetic; at `hospital_g` they carry the shortcut.
## Download and use
```bash
hf download slicepoint/slicepoint-hospitals --repo-type dataset --exclude "*/images/*" "*/masks/*" # tables + keys, ~10 MB
hf download slicepoint/slicepoint-hospitals --repo-type dataset --include "hospital_c/*" # one site with images
```
```bash
slicepoint assess --data hospital_b --site-id hospital_b \
--reference-dir hospital_a --reference-site-id hospital_a --html report.html
```
For federated-learning experiments, treat each `hospital_<x>` folder as one client;
`crosswalk.parquet` tells you where clients share patients.
## Source, license and citation
Derived from the **TotalSegmentator dataset v2.0.1** (Wasserthal J. et al., University
Hospital Basel, [doi:10.5281/zenodo.10047292](https://doi.org/10.5281/zenodo.10047292);
acquisition details from v3.0.0,
[doi:10.5281/zenodo.22688904](https://doi.org/10.5281/zenodo.22688904)), licensed
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Modified by Asher Informatics
PBC (sampled, perturbed as described above, re-identified, derived tables added) and
released under CC BY 4.0. Segmentations by the TotalSegmentator `total` model
(Apache-2.0). Embeddings by [Merlin](https://huggingface.co/stanfordmimi/Merlin) (MIT).
Please cite TotalSegmentator (Radiology: AI 2023,
[doi:10.1148/ryai.230024](https://doi.org/10.1148/ryai.230024)).
|