language:
- en
license: apache-2.0
size_categories:
- 1K<n<10M
task_categories:
- visual-question-answering
tags:
- medical
pretty_name: RadImageNet-VQA
dataset_info:
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path: alignment/train-*
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data_files:
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extra_gated_prompt: >-
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RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
Dataset Details
We introduce RadImageNet-VQA, a large-scale dataset designed for training and benchmarking radiologic VQA on CT and MRI exams. Built from the CT/MRI subset of RadImageNet and its expert-curated anatomical and pathological annotations, RadImageNet-VQA provides 750K images with 7.5M generated samples, including 750K medical captions for visual-text alignment and 6.75M question-answer pairs that span three radiology tasks: fine-grained pathology identification, anatomy recognition, and abnormality detection. The dataset includes open-ended, closed-ended, and multiple-choice questions across 8 anatomical regions and 97 pathologies, generated with prompt-based templates and constructed to probe visual-grounded understanding while minimizing text-only shortcut answering. For evaluation, we construct a stratified benchmark of 1,000 images with 9,000 question-answer pairs covering all tasks and question types.
Data Creation
RadImageNet-VQA was created to challenge multimodal models with tasks that demand radiology text-image understanding, pushing the boundaries of what these models can achieve in terms of perception and reasoning. The data for the RadImageNet-VQA dataset was build upon RadImageNet, a large expert-annotated medical imaging dataset in which each image is associated with a modality (CT, MRI, US), a body part (e.g., abdomen, hip, brain) and a pathology label. From this resource, we use the CT and MRI subsets to form the basis for generating clinically meaningful captions and VQA samples across anatomy, abnormality, and fine-grained pathology tasks.
Zero-shot Results
Zero-shot accuracies (%) of VLMs on RadImageNet-VQA benchmark. Results are reported across anatomy recognition, abnormality detection (Abn), and pathology identification using four question formats: Open (free-form), Closed+ (always 'yes' as true answer), Closed– (always 'no'), and MC (multiple-choice).
| Model | Anatomy | Abnormality | Pathology | Average | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Open | Closed+ | Closed– | MC | Closed | Open | Closed+ | Closed– | MC | ||
| General-purpose models | ||||||||||
| LLaVA-OneVision-Qwen2-7B | 48.4 | 82.7 | 81.3 | 88.7 | 49.8 | 16.0 | 55.3 | 61.3 | 33.6 | 57.5 |
| Qwen2.5-VL-3B-Instruct | 37.7 | 83.7 | 77.1 | 77.9 | 70.5 | 10.0 | 78.1 | 21.4 | 34.8 | 54.6 |
| Qwen2.5-VL-7B-Instruct | 37.5 | 84.9 | 79.1 | 80.5 | 69.5 | 9.8 | 69.2 | 47.4 | 30.1 | 56.4 |
| InternVL3.5-8B | 50.9 | 98.1 | 75.9 | 93.3 | 58.9 | 9.9 | 85.9 | 27.8 | 41.8 | 60.3 |
| InternVL3.5-14B | 56.6 | 98.2 | 74.4 | 89.9 | 74.4 | 11.7 | 86.7 | 33.7 | 47.1 | 63.6 |
| GPT-5 | 44.3 | 72.4 | 81.8 | 89.3 | 27.5 | 15.8 | 54.9 | 68.3 | 41.2 | 54.9 |
| Gemini 2.5 Pro | 65.7 | 76.5 | 81.9 | 88.8 | 17.8 | 21.1 | 50.2 | 30.1 | 44.4 | 52.9 |
| Medical-specialized models | ||||||||||
| LLaVA-Med-v1.5-mistral-7b | 44.3 | 89.9 | 55.3 | 58.1 | 22.4 | 10.2 | 41.8 | 66.6 | 26.4 | 48.2 |
| HuatuoGPT-Vision-7B | 45.4 | 82.5 | 89.0 | 88.3 | 60.6 | 13.6 | 65.5 | 69.2 | 44.6 | 48.9 |
| medgemma-4b-it | 62.9 | 76.4 | 82.5 | 84.8 | 55.4 | 30.6 | 54.2 | 77.4 | 36.8 | 51.5 |
| Lingshu-7B | 49.6 | 90.7 | 85.1 | 88.9 | 47.9 | 15.7 | 57.0 | 78.8 | 29.6 | 60.4 |
| Lingshu-32B | 45.2 | 75.5 | 92.1 | 89.3 | 54.5 | 14.4 | 46.4 | 88.8 | 31.7 | 59.8 |
Bold = best, italic = second best
Data Structure
Alignment Data
The alignment component contains single caption samples per image, intended to align visual content with concise clinical descriptions.
Each instance conceptually includes:
- an image
- a single prompt–response pair
- structured metadata
Fields:
id: unique sample identifierimage: relative path to the medical imageconversations: one human prompt and one descriptive responsemetadata: modality, anatomical location, abnormality flag, pathology label
The response provides a brief clinical description of the image.
Instruction Data
The instruction component contains multiple question–answer pairs per image and is intended for instruction tuning of multimodal models.
Each instance includes:
- an image
- one or more QA-style conversation turns
- structured metadata describing the task
Supported instruction types include image description, pathology identification, modality recognition, and anatomical localization.
Benchmark Data
The benchmark split is designed for standardized evaluation of medical VQA models.
It contains 9,000 question–answer pairs across 1,000 images and includes three question types:
- open-ended (free-form answers)
- closed-ended (yes/no)
- multiple-choice (options A–D)
Benchmark fields:
image: medical image referencequestion: question presented to the modelchoices: answer options (multiple-choice only)answer: ground-truth answerquestion_type: open, yes/no, or multiple-choicemetadata: modality, anatomy, pathology, and correctness labels
Metadata
Metadata fields provide structured clinical and contextual information:
modality: imaging modality (e.g., CT, MRI)location: anatomical regionis_abnormal: presence of pathologypathology: pathology categorycontent_type: task type (description, pathology, etc.)question_id: question template identifiercorrect_text: textual form of the correct answer (when applicable)
Data Splits
The dataset is organized into three configurations with training and validation splits:
| Alignment | Instruction Tuning | Benchmark | |||
|---|---|---|---|---|---|
| Train | Validation | Train | Validation | Test | |
| Samples | 750,009 | 83,668 | 750,009 | 83,668 | 9,000 |
| Images | 750,009 | 83,668 | 750,009 | 83,668 | 1,000 |
| QAs per image | 1 | 1 | ~9 | ~9 | 9 |
| Total QAs | 750K | 83K | 6.75M | 753K | 9K |
Acknowledgments
The dataset is built upon RadImageNet https://www.radimagenet.com/.
Citation
@inproceedings{
butsanets2025radimagenetvqa,
title={RadImageNet{VQA}: A Large-Scale {CT} and {MRI} Dataset for Medical Visual Question Answering},
author={L{\'e}o Butsanets and Charles Corbi{\`e}re and Julien Khlaut and Pierre Manceron and Corentin Dancette},
year={2025},
url={https://openreview.net/forum?id=khHKvZ9sLD},
}