README.md

---
license: other
license_name: physionet-credentialed-health-data-license
license_link: https://physionet.org/content/mimiciii/view-license/1.4/
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - medical
  - radiology
  - chest-x-ray
  - hard-negatives
  - mimic-cxr
pretty_name: MIMIC-CXR-Diff
size_categories:
  - 1M<n<10M
---

# MIMIC-CXR-Diff

Hard-negative VQA pairs mined from [MIMIC-Ext-VQA](https://physionet.org/content/mimic-ext-mimic-cxr-vqa/), a large-scale medical VQA dataset built on MIMIC-CXR chest X-rays.

Each row is a **pair** of visually similar images with the **same question type** but **different answers** — designed for evaluating and improving model robustness to subtle visual differences.

## Processing Pipeline

1. **Normalize** the MIMIC-Ext-VQA training split (290K samples → 260K after removing invalid entries).
2. **Embed** all images using [BiomedCLIP](https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224).
3. **Mine pairs** via approximate nearest-neighbor search (FAISS) on image embeddings.
4. **Filter** pairs:
   - Remove pairs where both sides reference the exact same image (`image_similarity = 1.0`).
   - Keep only pairs where the question type matches (`content_type_a == content_type_b`).
   - Keep only pairs where the answers differ (after whitespace stripping).

**Result**: 1,739,965 hard-negative pairs covering 109,415 unique images.

## Schema

| Column | Type | Description |
|---|---|---|
| `pair_id` | int | Unique sequential identifier (0-indexed) |
| `image_path_a` | string | Relative path to image A within the MIMIC-CXR directory |
| `image_path_b` | string | Relative path to image B within the MIMIC-CXR directory |
| `question_a` | string | VQA question for image A |
| `question_b` | string | VQA question for image B |
| `answer_a` | string | Ground-truth answer for image A |
| `answer_b` | string | Ground-truth answer for image B |
| `meta` | string (JSON) | Nested metadata (see below) |

### Meta Structure

```json
{
  "question_type": "presence",
  "image_similarity": 0.9833,
  "question_similarity": 0.9118,
  "image_a": {
    "image_id": "d0d24188-dda41b64-...",
    "subject_id": "18386740",
    "study_id": "56503182",
    "semantic_type": "verify",
    "template": "Is the ${object} showing indications of ${attribute}?",
    "template_program": "program_2",
    "template_arguments": {"object": {"0": "right lower lung zone"}, ...},
    "mimic_ext_vqa_idx": 7040
  },
  "image_b": { ... }
}
```

| Meta Field | Description |
|---|---|
| `question_type` | Shared question category (e.g., `presence`, `attribute`, `abnormality`) |
| `image_similarity` | BiomedCLIP cosine similarity between the two images |
| `question_similarity` | Sentence embedding cosine similarity between the two questions |
| `image_a` / `image_b` | Per-image metadata |
| `.image_id` | DICOM-derived image identifier |
| `.subject_id` | Patient identifier in MIMIC-CXR |
| `.study_id` | Study identifier in MIMIC-CXR |
| `.semantic_type` | Question semantic type (`verify`, `query`, `choose`) |
| `.template` | Question generation template |
| `.template_program` | Template program identifier |
| `.template_arguments` | Template slot-fill arguments |
| `.mimic_ext_vqa_idx` | Index into the original MIMIC-Ext-VQA `train.json` |

## Usage

```python
from datasets import load_dataset
import json

ds = load_dataset("mtybilly/MIMIC-CXR-Diff", split="train")

row = ds[0]
meta = json.loads(row["meta"])
print(row["question_a"], "→", row["answer_a"])
print(row["question_b"], "→", row["answer_b"])
print("Image similarity:", meta["image_similarity"])
```

> **Note**: This dataset contains metadata only. Images must be obtained separately from [MIMIC-CXR](https://physionet.org/content/mimic-cxr-jpg/) via PhysioNet (requires credentialed access). Image paths are relative to the MIMIC-CXR root directory.