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https://www.med-vqa.com/

html med-vqa.com collected 2026-10-01 13:05:22 UTC 12,363 bytes, 402 lines download raw bytes

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128            <a href="/slake/" itemprop="url">SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering</a>
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134                <time datetime="2021-01-31T04:51:25.000Z" itemprop="datePublished">Jan 31 2021</time>
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142            a minute read (About 162 words)
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148        <html><head></head><body><p><img src="images/slake.png"><br>The report of <strong>SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering</strong> [ISBI 2021]. We build a large bilingual dataset, SLAKE, with comprehensive semantic labels annotated by experienced physicians and a new structural medical knowledge base for Med-VQA. Besides, SLAKE includes richer modalities and covers more human body parts than the currently available dataset</p></body></html>
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162            <a href="https://www.med-vqa.com/GEMeX/" itemprop="url">GEMeX: GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis</a>
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168                <time datetime="2024-12-12T05:15:25.000Z" itemprop="datePublished">Dec 12 2024</time>
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176            a minute read (About 300 words)
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182        <html><head></head><body><p><img src="images/GEMex.jpg"><br>The report of <strong>GEMeX: GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis</strong> [ICCV 2025]. We introduce GEMeX, a large-scale Med-VQA dataset for chest X-rays, designed to support diverse question types and provide enhanced explainability for medical VQA systems. To our knowledge, it is the largest chest X-ray VQA dataset and the first Med-VQA dataset to embody the concept of multimodal explainability.</p></body></html>
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