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Datasets:
mugezhang
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medical-diff-vqa-longitudinal

Tasks:
Visual Question Answering
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
medical
radiology
chest-xray
mimic-cxr
longitudinal
temporal
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
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Medical-Diff-VQA Longitudinal

Dataset Description

This is an enhanced, filtered subset of Medical-Diff-VQA with pre-computed longitudinal imaging context.

Key Features

Pre-computed context images: Each question includes DICOM IDs for historical images Temporal information: Days from first visit for all images Most recent N=4 strategy: Context images are the 4 most recent studies before the reference pair Complete image metadata: View positions, dates, and paths included

Filtering Criteria

From the original Medical-Diff-VQA (700K questions):

  1. Question Type: Only "difference" subtype (164K questions)
  2. Longitudinal Context: Only patients with ≥1 additional study before the reference pair (124K questions)

This ensures every question can be evaluated in both:

  • Baseline condition: 2 reference images only
  • Longitudinal condition: 2 reference images + up to 4 historical context images

Dataset Statistics

  • Total questions: 123,980
  • Unique patients: 19,410
  • Train: 99,194 (80.0%)
  • Validation: 12,349 (10.0%)
  • Test: 12,437 (10.0%)

Context Image Distribution:

  • 1 context image: 29,871 questions (24.1%)
  • 2 context images: 19,119 questions (15.4%)
  • 3 context images: 13,556 questions (10.9%)
  • 4 context images: 61,434 questions (49.6%)
  • Average: 2.86 context images per question

Data Structure

Fields

Field Type Description
id int Unique question identifier
subject_id int MIMIC-CXR patient ID
question_type string Always "difference"
question string VQA question text
answer string Ground truth answer
split string train/val/test
num_context_images int Number of context images (1-4)
context_images JSON string Array of context image objects
main_image JSON string Current/study image object
ref_image JSON string Reference image object

Image Object Structure

Each image object (in context_images, main_image, ref_image) contains:

{
  "study_id": 55088298,
  "dicom_id": "61976388-5e534624-f6465079-76ea9caf-116f9938",
  "view_position": "PA",
  "study_date": "2177-10-02",
  "days_from_first_visit": 5,
  "image_path": "/fs/scratch/.../p18/p18936629/s55088298/61976388-...-.jpg"
}

Context Selection Strategy

Most Recent N=4: For each question, we select up to 4 most recent studies that occurred before both images in the reference pair.

Temporal Ordering

Context images are ordered chronologically (earliest to most recent), making it easy to understand disease progression.

Experimental Setup

This dataset supports comparing two conditions:

Condition Images Provided Expected Performance
Baseline 2 (ref + main) Lower baseline
Longitudinal 2 + N context (up to 6 total) Improved with context

The hypothesis: Providing longitudinal context helps VLMs better understand changes.

Source Data

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