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.

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.

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 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. Images via HuggingFace danjacobellis/chexpert.
MIMIC-CXR: Johnson et al. MIMIC-CXR-JPG v2.1.0. 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.

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).

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