| --- |
| license: cc-by-nc-4.0 |
| extra_gated_heading: "Acknowledge license and PhysioNet data use agreement" |
| extra_gated_description: "This dataset contains derived data from PhysioNet restricted-access datasets (MIMIC-CXR). By requesting access, you confirm that you have an active PhysioNet credentialed account and have signed the relevant data use agreements." |
| extra_gated_button_content: "Request access" |
| extra_gated_prompt: "You agree to not use this dataset to conduct experiments that cause harm to human subjects, and you confirm compliance with the PhysioNet data use agreement." |
| extra_gated_fields: |
| Full Name: text |
| Affiliation: text |
| Country: country |
| PhysioNet Username: text |
| I want to use this dataset for: |
| type: select |
| options: |
| - Research |
| - Education |
| - label: Other |
| value: other |
| I have a valid PhysioNet credentialed account with MIMIC-CXR access: checkbox |
| I agree to use this dataset for non-commercial use ONLY: checkbox |
| tags: |
| - medical-imaging |
| - chest-xray |
| - embeddings |
| - shortcut-detection |
| - fairness |
| - bias-detection |
| - celeba |
| - chexpert |
| - mimic-cxr |
| --- |
| |
| # ShortKIT-ML Benchmark Data |
|
|
| Pre-computed embeddings, metadata, and **full original dataset labels** for reproducing paper benchmarks. All embeddings were extracted with `seed=42` for full reproducibility. |
|
|
| ## Full Dataset Files (not just embeddings) |
|
|
| This repository includes the **complete original label/metadata files** for CheXpert and MIMIC-CXR — not only the embedding subsets used in our experiments: |
|
|
| | File | Rows | Description | |
| |------|------|-------------| |
| | `train.csv` | 223,414 | **Full CheXpert training set** — Path, Sex, Age, AP/PA, 14 diagnosis labels | |
| | `valid.csv` | 234 | **Full CheXpert validation set** — same schema | |
| | `mimic_cxr/mimic-cxr-2.0.0-chexpert.csv` | 227,827 | **Full MIMIC-CXR diagnosis labels** — 14 CheXpert-style labels per study | |
| | `mimic_cxr/mimic-cxr-2.0.0-metadata.csv` | 377,110 | **Full MIMIC-CXR DICOM metadata** — view position, rows, cols, study date | |
| | `chexpert_multibackbone/race_mapping.csv` | — | CheXpert patient-to-race mapping (from CHEXPERT DEMO) | |
|
|
| These are the same files distributed by Stanford (CheXpert) and PhysioNet (MIMIC-CXR). No rows have been filtered or removed. |
|
|
| ## Embedding Subsets (for benchmark reproduction) |
|
|
| The embedding files below are **subsets** extracted for our experiments (2,000 CheXpert samples, 1,491 MIMIC-CXR samples, 10,000 CelebA samples). |
|
|
| ``` |
| data/ |
| ├── chest_embeddings.npy # CheXpert MedCLIP embeddings (2000, 512) |
| ├── chest_labels.npy # Binary task labels (2000,) |
| ├── chest_group_labels.npy # Race groups: 0=ASIAN,1=BLACK,2=OTHER,3=WHITE |
| ├── chexpert_manifest.csv # CheXpert metadata (image_path, task_label, race, sex, age) |
| │ |
| ├── chexpert/ # CheXpert 8 backbones (from danjacobellis/chexpert) |
| │ ├── {backbone}_embeddings.npy # 8 backbones × 2000 samples each |
| │ ├── {backbone}_metadata.csv # sex, age, age_bin, race + 14 diagnoses per sample |
| │ └── chexpert_manifest.csv |
| │ |
| ├── chexpert_multibackbone/ # Same as chexpert/ with race_mapping.csv |
| │ ├── {backbone}_embeddings.npy |
| │ ├── {backbone}_metadata.csv |
| │ └── race_mapping.csv |
| │ |
| ├── mimic_cxr/ # MIMIC-CXR 4 backbones (from qml-mimic-cxr-embeddings) |
| │ ├── {backbone}_embeddings.npy # 4 backbones × 1491 samples each |
| │ ├── {backbone}_metadata.csv # race, sex, age, age_bin + 14 diagnoses per sample |
| │ ├── mimic_cxr_manifest.csv |
| │ ├── mimic-cxr-2.0.0-chexpert.csv # ← FULL dataset (227K studies) |
| │ └── mimic-cxr-2.0.0-metadata.csv # ← FULL dataset (377K DICOMs) |
| │ |
| └── celeba/ # CelebA (from torchvision, 10k subsample) |
| ├── celeba_real_embeddings.npy # (10000, 2048) ResNet-50 ImageNet |
| └── celeba_real_metadata.csv # gender + 40 CelebA attributes |
| ``` |
|
|
| ## Metadata CSV Format |
|
|
| All metadata CSVs share a common schema: |
|
|
| | Column | Type | Description | |
| |--------|------|-------------| |
| | `task_label` | int | Binary task label (0/1) | |
| | `sex` | str | Male / Female | |
| | `age` | float | Patient age | |
| | `age_bin` | str | Age group: <40, 40-60, 60-80, 80+ | |
| | `race` | str | WHITE, BLACK, ASIAN, OTHER (MIMIC-CXR only) | |
|
|
| **Per-diagnosis columns** (MIMIC-CXR and CheXpert multi-backbone): |
|
|
| | Column | Values | Description | |
| |--------|--------|-------------| |
| | `Atelectasis` | 1.0 / 0.0 / NaN | Positive / Negative / Unlabeled | |
| | `Cardiomegaly` | 1.0 / 0.0 / NaN | | |
| | `Consolidation` | 1.0 / 0.0 / NaN | | |
| | `Edema` | 1.0 / 0.0 / NaN | | |
| | `Enlarged Cardiomediastinum` | 1.0 / 0.0 / NaN | | |
| | `Fracture` | 1.0 / 0.0 / NaN | | |
| | `Lung Lesion` | 1.0 / 0.0 / NaN | | |
| | `Lung Opacity` | 1.0 / 0.0 / NaN | | |
| | `No Finding` | 1.0 / 0.0 / NaN | | |
| | `Pleural Effusion` | 1.0 / 0.0 / NaN | | |
| | `Pleural Other` | 1.0 / 0.0 / NaN | | |
| | `Pneumonia` | 1.0 / 0.0 / NaN | | |
| | `Pneumothorax` | 1.0 / 0.0 / NaN | | |
| | `Support Devices` | 1.0 / 0.0 / NaN | | |
|
|
| ## Reproduction Scripts |
|
|
| | Dataset | Extraction Script | Prerequisites | |
| |---------|-------------------|---------------| |
| | CheXpert (MedCLIP) | `scripts/setup_chexpert_data.py` | Existing `data/chest_*.npy` | |
| | CheXpert (multi-backbone) | `scripts/extract_chexpert_hf_multibackbone.py --device mps --parallel` | `pip install datasets`, network access | |
| | MIMIC-CXR (embeddings) | `scripts/setup_mimic_cxr_data.py` | [qml-mimic-cxr-embeddings](https://huggingface.co/datasets/MITCriticalData/qml-mimic-cxr-embeddings) repo | |
| | MIMIC-CXR (diagnosis labels) | `scripts/join_mimic_diagnosis_labels.py` | PhysioNet `mimic-cxr-2.0.0-chexpert.csv` | |
| | CelebA | `scripts/extract_celeba_embeddings.py` | `pip install datasets`, network access | |
|
|
| ## Data Provenance |
|
|
| - **CheXpert**: Stanford ML Group. [CheXpert: A Large Chest Radiograph Dataset](https://stanfordmlgroup.github.io/competitions/chexpert/). Images via HuggingFace `danjacobellis/chexpert`. |
| - **MIMIC-CXR**: Johnson et al. [MIMIC-CXR-JPG v2.1.0](https://physionet.org/content/mimic-cxr-jpg/2.1.0/). Embeddings via `MITCriticalData/qml-mimic-cxr-embeddings`. Diagnosis labels from PhysioNet (CheXpert labeler output). Demographics from MIMIC-IV via `subject_id` join. |
| - **CelebA**: Liu et al. [Large-scale CelebFaces Attributes Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). |
|
|
| ## Notes |
|
|
| - The `_cache/` subdirectory in `chexpert_multibackbone/` contains raw PIL images cached during extraction. It is excluded from the HuggingFace upload (large binary pickle). Re-run the extraction script to regenerate. |
| - MIMIC-CXR `*_metadata_orig.csv` files are pre-diagnosis-join backups. The `*_metadata.csv` files contain the joined version with 14 diagnosis columns. |
| - All random seeds are fixed to 42. CheXpert multi-backbone uses the first 2000 samples from the streaming iterator (deterministic ordering from HuggingFace). |
|
|
