| --- |
| 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)). |
|
|
