RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
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| Format: | Preprint |
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2025
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| _version_ | 1866917367815077888 |
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| author | Butsanets, Léo Corbière, Charles Khlaut, Julien Manceron, Pierre Dancette, Corentin |
| author_facet | Butsanets, Léo Corbière, Charles Khlaut, Julien Manceron, Pierre Dancette, Corentin |
| contents | In this work, we introduce RadImageNet-VQA, a large-scale dataset designed to advance radiologic visual question answering (VQA) on CT and MRI exams. Existing medical VQA datasets are limited in scale, dominated by X-ray imaging or biomedical illustrations, and often prone to text-based shortcuts. RadImageNet-VQA is built from expert-curated annotations and provides 750K images paired with 7.5M question-answer samples. It covers three key tasks - abnormality detection, anatomy recognition, and pathology identification - spanning eight anatomical regions and 97 pathology categories, and supports open-ended, closed-ended, and multiple-choice questions. Extensive experiments show that state-of-the-art vision-language models still struggle with fine-grained pathology identification, particularly in open-ended settings and even after fine-tuning. Text-only analysis further reveals that model performance collapses to near-random without image inputs, confirming that RadImageNet-VQA is free from linguistic shortcuts. The full dataset and benchmark are publicly available at https://huggingface.co/datasets/raidium/RadImageNet-VQA. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_17396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering Butsanets, Léo Corbière, Charles Khlaut, Julien Manceron, Pierre Dancette, Corentin Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language In this work, we introduce RadImageNet-VQA, a large-scale dataset designed to advance radiologic visual question answering (VQA) on CT and MRI exams. Existing medical VQA datasets are limited in scale, dominated by X-ray imaging or biomedical illustrations, and often prone to text-based shortcuts. RadImageNet-VQA is built from expert-curated annotations and provides 750K images paired with 7.5M question-answer samples. It covers three key tasks - abnormality detection, anatomy recognition, and pathology identification - spanning eight anatomical regions and 97 pathology categories, and supports open-ended, closed-ended, and multiple-choice questions. Extensive experiments show that state-of-the-art vision-language models still struggle with fine-grained pathology identification, particularly in open-ended settings and even after fine-tuning. Text-only analysis further reveals that model performance collapses to near-random without image inputs, confirming that RadImageNet-VQA is free from linguistic shortcuts. The full dataset and benchmark are publicly available at https://huggingface.co/datasets/raidium/RadImageNet-VQA. |
| title | RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2512.17396 |