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--- |
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language: |
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- en |
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license: apache-2.0 |
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size_categories: |
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- 1K<n<10M |
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task_categories: |
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- visual-question-answering |
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tags: |
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- medical |
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|
pretty_name: RadImageNet-VQA |
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dataset_info: |
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- config_name: alignment |
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features: |
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- name: image |
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|
dtype: image |
|
|
- name: conversations |
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|
list: |
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|
- name: from |
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|
dtype: string |
|
|
- name: value |
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|
dtype: string |
|
|
- name: metadata |
|
|
struct: |
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|
- name: content_type |
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|
dtype: string |
|
|
- name: correct_text |
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|
dtype: 'null' |
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|
- name: is_abnormal |
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|
dtype: bool |
|
|
- name: location |
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|
dtype: string |
|
|
- name: modality |
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|
dtype: string |
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|
- name: pathology |
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|
dtype: string |
|
|
- name: question_id |
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|
dtype: string |
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|
splits: |
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|
- name: train |
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|
num_bytes: 29401649909 |
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|
num_examples: 750009 |
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|
- name: val |
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|
num_bytes: 3175441830 |
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|
num_examples: 83668 |
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|
download_size: 38405331105 |
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|
dataset_size: 32577091739 |
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|
- config_name: benchmark |
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|
features: |
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|
- name: image |
|
|
dtype: image |
|
|
- name: question |
|
|
dtype: string |
|
|
- name: choices |
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|
list: string |
|
|
- name: answer |
|
|
dtype: string |
|
|
- name: question_type |
|
|
dtype: string |
|
|
- name: metadata |
|
|
struct: |
|
|
- name: content_type |
|
|
dtype: string |
|
|
- name: correct_text |
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|
dtype: string |
|
|
- name: is_abnormal |
|
|
dtype: bool |
|
|
- name: location |
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|
dtype: string |
|
|
- name: modality |
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|
dtype: string |
|
|
- name: pathology |
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|
dtype: string |
|
|
- name: question_id |
|
|
dtype: string |
|
|
splits: |
|
|
- name: test |
|
|
num_bytes: 414947216 |
|
|
num_examples: 9000 |
|
|
download_size: 361133763 |
|
|
dataset_size: 414947216 |
|
|
- config_name: instruct |
|
|
features: |
|
|
- name: image |
|
|
dtype: image |
|
|
- name: conversations |
|
|
list: |
|
|
- name: from |
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|
dtype: string |
|
|
- name: value |
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|
dtype: string |
|
|
- name: metadata |
|
|
struct: |
|
|
- name: content_type |
|
|
dtype: string |
|
|
- name: correct_text |
|
|
dtype: string |
|
|
- name: is_abnormal |
|
|
dtype: bool |
|
|
- name: location |
|
|
dtype: string |
|
|
- name: modality |
|
|
dtype: string |
|
|
- name: pathology |
|
|
dtype: string |
|
|
- name: question_id |
|
|
dtype: string |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 29904541796 |
|
|
num_examples: 750009 |
|
|
- name: val |
|
|
num_bytes: 3231558586 |
|
|
num_examples: 83668 |
|
|
download_size: 38424398344 |
|
|
dataset_size: 33136100382 |
|
|
configs: |
|
|
- config_name: alignment |
|
|
data_files: |
|
|
- split: train |
|
|
path: alignment/train-* |
|
|
- split: val |
|
|
path: alignment/val-* |
|
|
- config_name: instruct |
|
|
data_files: |
|
|
- split: train |
|
|
path: instruct/train-* |
|
|
- split: val |
|
|
path: instruct/val-* |
|
|
- config_name: benchmark |
|
|
data_files: |
|
|
- split: test |
|
|
path: benchmark/test-* |
|
|
|
|
|
extra_gated_prompt: >- |
|
|
### RADIMAGENET LLC Dataset Research Use Agreement |
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1. RadImageNet grants you permission, upon your agreeing to the terms of the Research Use |
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Agreement, to view and use the Dataset for personal, non-commercial (e.g., academic) research |
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purposes only. Any commercial use, sale, or other monetization, by you or your affiliates, is |
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strictly prohibited under any and all circumstances. |
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2. Other than any limited rights expressly granted herein to you, RadImageNet retains all rights, |
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title, and interest in the Dataset. |
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3. You may make a verbatim copy of the Dataset for non-commercial research use as permitted |
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in the Research Use Agreement. You may not alter this verbatim copy for any reason. If another |
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user within your organization wishes to use the Dataset, they must register as an individual user |
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and comply with all the terms of the Research Use Agreement. |
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4. YOU MAY NOT DISTRIBUTE, PUBLISH, OR REPRODUCE A COPY of any portion, |
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including the entirety, of the Dataset to anyone without express and specific prior written |
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5. YOU MAY NOT SHARE THE DOWNLOAD LINK to the Dataset with others. For example, |
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if someone other than you within your organization wishes to use or view the Dataset, they must |
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register as an individual user and agree to and comply with all the terms of the Research Use |
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Agreement. |
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6. You must not modify, reverse engineer, decompile, or create derivative works from the |
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Dataset. You must not remove or alter any copyright or other proprietary notices in the Dataset. |
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7. The Dataset has not been reviewed or approved by the Food and Drug Administration, or any |
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other regulatory agency of the United States of America. The Dataset is being provided to you |
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strictly and only for non-clinical, research use. In no event shall data or images generated |
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use, directly or indirectly, in whole or in part, of the Dataset in the diagnosis or provision of |
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8. THE DATASET IS PROVIDED “AS IS,” AND RADIMAGENET AND ITS |
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COLLABORATORS MAKE NO WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT |
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NOT LIMITED TO WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY |
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PARTICULAR PURPOSE,2 NOR DO THEY ASSUME ANY LIABILITY OR |
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RESPONSIBILITY FOR THE USE OF THE DATASET. |
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9. You will not attempt to identify or re-identify any of the individual data subjects (e.g., |
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patients). Identification or re-identification of individuals is strictly prohibited. Any |
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identification or re-identification of any individual data subject shall be immediately reported to |
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10. Any violation of the Research Use Agreement or other impermissible use shall be grounds |
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for immediate termination of use of the Dataset. It is your duty to promptly report to |
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RadImageNet any knowledge of any violation at any time. In the event that RadImageNet |
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determines that you have violated this Research Use Agreement or made other impermissible use |
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of the Dataset, RadImageNet may direct that you immediately return all copies of the Dataset |
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and retain no copies thereof. RadImageNet may do this even if you did not cause the violation or |
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impermissible use. |
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In consideration for your agreement to the terms and conditions contained in the Research Use |
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Agreement, RadImageNet grants you limited permission to view and use the Dataset for |
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personal, non-commercial research, as described herein. You may not otherwise copy, reproduce, |
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retransmit, distribute, publish, commercially exploit or otherwise transfer any material from or |
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related to the Dataset. |
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You may use the Dataset for legal purposes only. |
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You agree to indemnify and hold RadImageNet harmless from and not liable in any way for any |
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Dataset or your violation or role in violation of the Research Use Agreement. You agree to fully |
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cooperate in RadImageNet’s defense against any such claims. These terms and all other terms of |
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the Research Use Agreement shall be governed by and interpreted in accordance with the laws of |
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New York State. |
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extra_gated_fields: |
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Name: text |
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Title: text |
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Date : date_picker |
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By clicking Submit below I accept the terms of this RADIMAGENET LLC Dataset Research Use Agreement (hereinafter “the Research Use Agreement”), as well as to the Terms of Use of the RADIMAGENET LLC (hereinafter “RadImageNet”) website as posted and updated periodically : checkbox |
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extra_gated_button_content: Submit |
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--- |
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|
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|
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|
|
|
<div align="center"> |
|
|
<img src="https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/62cdea59a9be5c195561c2b8/JaS4YslW9wFR8dZ7LMawz.png" width="40%" alt="Raidium" /> |
|
|
</div> |
|
|
<hr> |
|
|
|
|
|
<hr> |
|
|
|
|
|
<h2> |
|
|
<p align="center"> |
|
|
<h1 align="center">RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering</h1> |
|
|
</p> |
|
|
</h2> |
|
|
|
|
|
<p align="center"> |
|
|
<a href="https://openreview.net/forum?id=khHKvZ9sLD"><b>📖 Paper </b></a> |
|
|
</p> |
|
|
<hr> |
|
|
|
|
|
## Dataset Details |
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|
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](https://pubs.rsna.org/doi/full/10.1148/ryai.210315) 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. |
|
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|
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|
<div align="center"> |
|
|
<img src="https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/680266d8ef7a20a062c7e40d/bfgLBUgoUCWOJL4Ss-4qu.png" width="100%" alt="Raidium" /> |
|
|
</div> |
|
|
|
|
|
--- |
|
|
## 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. |
|
|
|
|
|
<div align="center"> |
|
|
<img src="https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/680266d8ef7a20a062c7e40d/GZGe4kQ5PeZwk8p4Ola_D.png" width="100%" alt="Raidium" /> |
|
|
</div> |
|
|
|
|
|
## 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). |
|
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|
|
| Model | | Anatomy | | | Abnormality | | Pathology | | | Average | |
|
|
|-------|------|---------|--------|------|-------|------|---------|--------|------|-----| |
|
|
| | Open | Closed+ | Closed– | MC | Closed | Open | Closed+ | Closed– | MC | | |
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| **General-purpose models** | | | | | | | | | | | |
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|
| 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 | |
|
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| InternVL3.5-14B | 56.6 | **98.2** | 74.4 | 89.9 | **74.4** | 11.7 | **86.7** | 33.7 | **47.1** | **63.6** | |
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| GPT-5 | 44.3 | 72.4 | 81.8 | 89.3 | 27.5 | 15.8 | 54.9 | 68.3 | 41.2 | 54.9 | |
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| Gemini 2.5 Pro | **65.7** | 76.5 | 81.9 | 88.8 | 17.8 | _21.1_ | 50.2 | 30.1 | 44.4 | 52.9 | |
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|
| **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 | |
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|
| medgemma-4b-it | _62.9_ | 76.4 | 82.5 | 84.8 | 55.4 | **30.6** | 54.2 | 77.4 | 36.8 | 51.5 | |
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|
| Lingshu-7B | 49.6 | 90.7 | 85.1 | 88.9 | 47.9 | 15.7 | 57.0 | _78.8_ | 29.6 | _60.4_ | |
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| Lingshu-32B | 45.2 | 75.5 | **92.1** | _89.3_ | 54.5 | 14.4 | 46.4 | **88.8** | 31.7 | 59.8 | |
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**Bold** = best, *italic* = second best |
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|
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## Data Structure |
|
|
### Alignment Data |
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|
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The alignment component contains single caption samples per image, intended to align visual content with concise clinical descriptions. |
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Each instance conceptually includes: |
|
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- an image |
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- a single prompt–response pair |
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- structured metadata |
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**Fields:** |
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- `id`: unique sample identifier |
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- `image`: relative path to the medical image |
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- `conversations`: one human prompt and one descriptive response |
|
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- `metadata`: modality, anatomical location, abnormality flag, pathology label |
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The response provides a brief clinical description of the image. |
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--- |
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|
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### Instruction Data |
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The instruction component contains multiple question–answer pairs per image and is intended for instruction tuning of multimodal models. |
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Each instance includes: |
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- an image |
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- one or more QA-style conversation turns |
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- structured metadata describing the task |
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Supported instruction types include image description, pathology identification, modality recognition, and anatomical localization. |
|
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|
|
|
--- |
|
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|
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### Benchmark Data |
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The benchmark split is designed for standardized evaluation of medical VQA models. |
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It contains 9,000 question–answer pairs across 1,000 images and includes three question types: |
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- open-ended (free-form answers) |
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- closed-ended (yes/no) |
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- multiple-choice (options A–D) |
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**Benchmark fields:** |
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- `image`: medical image reference |
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- `question`: question presented to the model |
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- `choices`: answer options (multiple-choice only) |
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- `answer`: ground-truth answer |
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- `question_type`: open, yes/no, or multiple-choice |
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- `metadata`: modality, anatomy, pathology, and correctness labels |
|
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|
|
|
--- |
|
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|
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### Metadata |
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Metadata fields provide structured clinical and contextual information: |
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- `modality`: imaging modality (e.g., CT, MRI) |
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- `location`: anatomical region |
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- `is_abnormal`: presence of pathology |
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- `pathology`: pathology category |
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- `content_type`: task type (description, pathology, etc.) |
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- `question_id`: question template identifier |
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- `correct_text`: textual form of the correct answer (when applicable) |
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|
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### Data Splits |
|
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The dataset is organized into three configurations with training and validation splits: |
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|
| | Alignment | | Instruction Tuning | | Benchmark | |
|
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|-------------------------|:---------:|:---------:|:------------------:|:---------:|:---------:| |
|
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| | Train | Validation | Train | Validation | Test | |
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| Samples | 750,009 | 83,668 | 750,009 | 83,668 | 9,000 | |
|
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| Images | 750,009 | 83,668 | 750,009 | 83,668 | 1,000 | |
|
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| QAs per image | 1 | 1 | ~9 | ~9 | 9 | |
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| Total QAs | 750K | 83K | 6.75M | 753K | 9K | |
|
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|
|
|
## Acknowledgments |
|
|
|
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|
The dataset is built upon RadImageNet https://www.radimagenet.com/. |
|
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|
|
|
## Citation |
|
|
|
|
|
``` |
|
|
@inproceedings{ |
|
|
butsanets2025radimagenetvqa, |
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|
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}, |
|
|
} |
|
|
``` |
