| | --- |
| | 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_<N>/` |
| | - **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). |
| |
|
