--- license: cc-by-nc-nd-4.0 tags: - medical-imaging - chest-xray - embeddings - mimic-cxr - coreset-selection - quantum-machine-learning --- # 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**: ~1,999–2,372 samples per strategy (varies by seed) - **File Format**: Parquet (and legacy Pickle for CLIP-BioMed) ## Coreset Selection Strategies Each seed contains three coreset selection strategies. Sample counts shown are for seed_0; exact counts vary slightly by seed: | Strategy | Name | Samples (seed_0) | 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/20-seeds/seed_/` - **Format**: Parquet (`.parquet`) - **Files** (seed_0): - `data_type5_n1998_seed0_rad_dino.parquet` (1,998 samples) - `data_type9_n2370_seed0_rad_dino.parquet` (2,370 samples) - `data_type11_n2370_seed0_rad_dino.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_type5_insurance.pkl │ │ ├── data_type9_insurance.pkl │ │ ├── data_type9_insurance_2371rows.pkl │ │ ├── data_type11_insurance.pkl │ │ ├── data_type11_insurance_2371rows.pkl │ │ └── models/ │ │ ├── global_stats_100_type{9,11}_2371rows.npz │ │ └── svd_components_100_type{9,11}_2371rows.npz │ ├── data-cleaned-pca-500/ │ │ └── (same structure as pca-100, with 500-dim variants) │ ├── data-cleaned-pca-1000/ │ │ └── (same structure, with 1000-dim variants) │ ├── data-cleaned-pca-1999/ │ │ └── (same structure, with 1999-dim variants) │ └── 20-seeds/ │ ├── seed_0/ │ │ ├── data_type5_n1999.parquet │ │ ├── data_type9_n2371.parquet │ │ └── data_type11_n2371.parquet │ └── seed_1/ ... seed_19/ ├── medsiglip-448-embeddings/ │ ├── data_type5_n1999_seed0_medsiglip_448.parquet │ ├── data_type9_n2371_seed0_medsiglip_448.parquet │ ├── data_type11_n2371_seed0_medsiglip_448.parquet │ └── 20-seeds/ │ ├── seed_0/ │ │ ├── data_type5_n1999.parquet │ │ ├── data_type9_n2371.parquet │ │ └── data_type11_n2371.parquet │ └── seed_1/ ... seed_19/ ├── 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 │ └── 20-seeds/ │ ├── seed_0/ │ │ ├── data_type5_n1999_cls_embedding.parquet │ │ ├── data_type5_n1999_gap_embedding.parquet │ │ ├── data_type9_n2371_cls_embedding.parquet │ │ ├── data_type9_n2371_gap_embedding.parquet │ │ ├── data_type11_n2371_cls_embedding.parquet │ │ └── data_type11_n2371_gap_embedding.parquet │ └── seed_1/ ... seed_19/ ├── 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 │ └── 20-seeds/ │ ├── seed_0/ │ │ ├── data_type5_n1999_cls_embedding.parquet │ │ ├── data_type5_n1999_gap_embedding.parquet │ │ ├── data_type9_n2371_cls_embedding.parquet │ │ ├── data_type9_n2371_gap_embedding.parquet │ │ ├── data_type11_n2371_cls_embedding.parquet │ │ └── data_type11_n2371_gap_embedding.parquet │ └── seed_1/ ... seed_19/ ├── rad-dino-embeddings/ │ └── 20-seeds/ │ ├── seed_0/ │ │ ├── data_type5_n1998_seed0_rad_dino.parquet │ │ ├── data_type9_n2370_seed0_rad_dino.parquet │ │ └── data_type11_n2370_seed0_rad_dino.parquet │ └── seed_1/ ... seed_19/ └── tests/ ├── README.md ├── verify_all_embeddings.py ├── verify_basic_embeddings.py └── verify_rad_dino.py ``` ## Data Format Parquet files (ViT-16, ViT-32, MedSigLIP, RAD-DINO) 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) follow the same structure. ### Loading Example ```python 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).