MITCriticalData/qml-mimic-cxr-embeddings

MIMIC-CXR Embeddings Dataset

This dataset contains pre-extracted embeddings from MIMIC-CXR chest X-ray images using multiple state-of-the-art vision models. The embeddings are organized by coreset selection strategies for efficient training of quantum machine learning models.

Dataset Overview

• Source: MIMIC-CXR Database
• Total Seeds: 20 (seed_0 through seed_19)
• Coreset Strategies: 3 per seed
• Embedding Models: 5 vision transformer architectures
• Total Samples: Up to 2,371 samples per strategy
• File Format: Parquet and Pickle

Coreset Selection Strategies

Each seed contains three coreset selection strategies:

Strategy	Name	Samples	Description
5	PathologyStratifiedClean	1,999	Stratified sampling based on pathology labels
9	GradMatch	2,371	Gradient matching for representative subset selection
11	Uncertainty	2,371	Uncertainty-based active learning sample selection

Embedding Types (ViT-16 and ViT-32)

For ViT-Base-Patch16-224 and ViT-Base-Patch32-224, two embedding variants are provided per data type, distinguished by filename suffix:

_cls_embedding — CLS Token Embedding

The standard 768-dim representation extracted from the [CLS] token of the final transformer layer. This is the model's global summary vector used in classification tasks.

_gap_embedding — Multi-Layer Global Average Pooling

A richer 768-dim representation computed by pooling patch token hidden states across the last 4 transformer layers:

1. Extract patch token hidden states from the last 4 transformer blocks (CLS token excluded)
2. Stack into shape [4, num_patches, 768]
3. Mean-pool across the layer dimension → [num_patches, 768]
4. Mean-pool across the patch dimension → [768]

Model	Patch tokens per image	Layers pooled
ViT-Base-Patch16-224	196 (14 × 14)	Last 4 of 12
ViT-Base-Patch32-224	49 (7 × 7)	Last 4 of 12

Embedding Models

1. CLIP-BioMed

• Path: clip-biomed-embeddings/
• Format: Pickle (.pkl)
• Files:
  • data_type5_insurance.pkl (1,999 samples)
  • data_type9_insurance_2371rows.pkl (2,371 samples)
  • data_type11_insurance_2371rows.pkl (2,371 samples)

2. MedSigLIP-448

• Path: medsiglip-448-embeddings/
• Format: Parquet (.parquet)
• Embedding: CLS token (1,152-dim via google/siglip-so400m-patch14-384)
• Files (seed_0):
  • data_type5_n1999_seed0_medsiglip_448.parquet (1,999 samples)
  • data_type9_n2371_seed0_medsiglip_448.parquet (2,371 samples)
  • data_type11_n2371_seed0_medsiglip_448.parquet (2,371 samples)

3. ViT-Base-Patch32-224

• Path: vit-base-patch32-224-embeddings/
• Format: Parquet (.parquet)
• Embedding dimension: 768
• Files (seed_0):
  • data_type5_n1999_seed0_vit_base_patch32_224_cls_embedding.parquet — CLS token
  • data_type5_n1999_seed0_vit_base_patch32_224_gap_embedding.parquet — Multi-layer GAP
  • data_type9_n2371_seed0_vit_base_patch32_224_cls_embedding.parquet
  • data_type9_n2371_seed0_vit_base_patch32_224_gap_embedding.parquet
  • data_type11_n2371_seed0_vit_base_patch32_224_cls_embedding.parquet
  • data_type11_n2371_seed0_vit_base_patch32_224_gap_embedding.parquet

4. ViT-Base-Patch16-224

• Path: vit-base-patch16-224-embeddings/
• Format: Parquet (.parquet)
• Embedding dimension: 768
• Files (seed_0):
  • data_type5_n1999_seed0_vit_base_patch16_224_cls_embedding.parquet — CLS token
  • data_type5_n1999_seed0_vit_base_patch16_224_gap_embedding.parquet — Multi-layer GAP
  • data_type9_n2371_seed0_vit_base_patch16_224_cls_embedding.parquet
  • data_type9_n2371_seed0_vit_base_patch16_224_gap_embedding.parquet
  • data_type11_n2371_seed0_vit_base_patch16_224_cls_embedding.parquet
  • data_type11_n2371_seed0_vit_base_patch16_224_gap_embedding.parquet

5. RAD-DINO

• Path: rad-dino-embeddings/
• Format: Pickle (.pkl) and Parquet (.parquet)
• Files:
  • data_type5_n1998_seed0_rad_dino.pkl / .parquet (1,998 samples)
  • data_type9_n2370_seed0_rad_dino.pkl / .parquet (2,370 samples)
  • data_type11_n2370_seed0_rad_dino.pkl / .parquet (2,370 samples)
• Note: 1 sample missing per strategy (see Verification Status below)

Folder Structure

qml-mimic-cxr-embeddings/
├── coreset-ids/
│   ├── seed_0/
│   │   ├── coreset-has_pathology-5-PathologyStratifiedClean-seed_0.txt
│   │   ├── coreset-has_pathology-9-GradMatch-seed_0.txt
│   │   └── coreset-has_pathology-11-Uncertainty-seed_0.txt
│   └── seed_1/ ... seed_19/
├── clip-biomed-embeddings/
│   ├── README.md
│   ├── data_type5_insurance.pkl
│   ├── data_type9_insurance_2371rows.pkl
│   ├── data_type11_insurance_2371rows.pkl
│   ├── data-cleaned-pca-100/
│   ├── data-cleaned-pca-500/
│   ├── data-cleaned-pca-1000/
│   └── data-cleaned-pca-1999/
├── medsiglip-448-embeddings/
│   ├── data_type5_n1999_seed0_medsiglip_448.parquet
│   ├── data_type9_n2371_seed0_medsiglip_448.parquet
│   └── data_type11_n2371_seed0_medsiglip_448.parquet
├── vit-base-patch32-224-embeddings/
│   ├── data_type5_n1999_seed0_vit_base_patch32_224_cls_embedding.parquet
│   ├── data_type5_n1999_seed0_vit_base_patch32_224_gap_embedding.parquet
│   ├── data_type9_n2371_seed0_vit_base_patch32_224_cls_embedding.parquet
│   ├── data_type9_n2371_seed0_vit_base_patch32_224_gap_embedding.parquet
│   ├── data_type11_n2371_seed0_vit_base_patch32_224_cls_embedding.parquet
│   ├── data_type11_n2371_seed0_vit_base_patch32_224_gap_embedding.parquet
│   └── feature_maps/
│       ├── data_type5_n1999_seed0_vit_base_patch32_224_grid.pkl
│       ├── data_type9_n2371_seed0_vit_base_patch32_224_grid.pkl
│       └── data_type11_n2371_seed0_vit_base_patch32_224_grid.pkl
├── vit-base-patch16-224-embeddings/
│   ├── data_type5_n1999_seed0_vit_base_patch16_224_cls_embedding.parquet
│   ├── data_type5_n1999_seed0_vit_base_patch16_224_gap_embedding.parquet
│   ├── data_type9_n2371_seed0_vit_base_patch16_224_cls_embedding.parquet
│   ├── data_type9_n2371_seed0_vit_base_patch16_224_gap_embedding.parquet
│   ├── data_type11_n2371_seed0_vit_base_patch16_224_cls_embedding.parquet
│   ├── data_type11_n2371_seed0_vit_base_patch16_224_gap_embedding.parquet
│   └── feature_maps/
│       ├── data_type5_n1999_seed0_vit_base_patch16_224_grid.pkl
│       ├── data_type9_n2371_seed0_vit_base_patch16_224_grid.pkl
│       └── data_type11_n2371_seed0_vit_base_patch16_224_grid.pkl
├── rad-dino-embeddings/
│   ├── data_type5_n1998_seed0_rad_dino.pkl
│   ├── data_type5_n1998_seed0_rad_dino.parquet
│   ├── data_type9_n2370_seed0_rad_dino.pkl
│   ├── data_type9_n2370_seed0_rad_dino.parquet
│   ├── data_type11_n2370_seed0_rad_dino.pkl
│   └── data_type11_n2370_seed0_rad_dino.parquet
└── tests/
    ├── README.md
    ├── verify_all_embeddings.py
    ├── verify_basic_embeddings.py
    └── verify_rad_dino.py

Data Format

Parquet files (ViT-16, ViT-32, MedSigLIP) contain a pandas DataFrame where:

• embedding: Pre-extracted feature vector (as a list of floats) from the respective model/variant
• Metadata columns: dicom_id, subject_id, study_id, and additional MIMIC-CXR metadata

Pickle files (CLIP-BioMed, RAD-DINO) follow the same structure.

Loading Example

import pandas as pd

# Load a CLS embedding (parquet)
df = pd.read_parquet(
    'vit-base-patch16-224-embeddings/'
    'data_type5_n1999_seed0_vit_base_patch16_224_cls_embedding.parquet'
)
embeddings = df['embedding'].tolist()  # list of 768-dim vectors

# Load a GAP embedding (parquet)
df_gap = pd.read_parquet(
    'vit-base-patch16-224-embeddings/'
    'data_type5_n1999_seed0_vit_base_patch16_224_gap_embedding.parquet'
)
gap_embeddings = df_gap['embedding'].tolist()  # list of 768-dim vectors

# Load from HuggingFace Hub directly
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id='MITCriticalData/qml-mimic-cxr-embeddings',
    filename='vit-base-patch16-224-embeddings/data_type9_n2371_seed0_vit_base_patch16_224_gap_embedding.parquet',
    repo_type='dataset'
)
df = pd.read_parquet(path)

Verification Status (seed_0)

All seed_0 coreset IDs have been verified against extracted embeddings:

Embedding Type	Strategy 5	Strategy 9	Strategy 11
CLIP-BioMed	✓ 100% (1,999/1,999)	✓ 100% (2,371/2,371)	✓ 100% (2,371/2,371)
MedSigLIP-448	✓ 100% (1,999/1,999)	✓ 100% (2,371/2,371)	✓ 100% (2,371/2,371)
ViT-Patch32 CLS	✓ 100% (1,999/1,999)	✓ 100% (2,371/2,371)	✓ 100% (2,371/2,371)
ViT-Patch32 GAP	✓ 100% (1,999/1,999)	✓ 100% (2,371/2,371)	✓ 100% (2,371/2,371)
ViT-Patch16 CLS	✓ 100% (1,999/1,999)	✓ 100% (2,371/2,371)	✓ 100% (2,371/2,371)
ViT-Patch16 GAP	✓ 100% (1,999/1,999)	✓ 100% (2,371/2,371)	✓ 100% (2,371/2,371)
RAD-DINO	✓ 99.95% (1,998/1,999)	✓ 99.96% (2,370/2,371)	✓ 99.96% (2,370/2,371)

RAD-DINO Missing Samples:

• Strategy 5: db806824-34de7587-691208b6-19301aaa-15cca66c
• Strategy 9: 1d413540-516c7ce1-0a64dfe2-78c7b93e-808b2fce
• Strategy 11: 669089f6-b0ff4487-f652652d-80e2925d-7e2b2511

License

This dataset is released under CC-BY-NC-ND-4.0 (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International).