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<title>Visual Question Answering (VQA) for Medical Imaging</title>
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<h1 class="title">Visual Question Answering (VQA) for Medical Imaging</h1>
<h2 class="subtitle">Kalbe Digital Lab</h2>
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<h3 class="overview-heading"><span class="vl">Overview</span></h3>
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<p class="overview-content">
This project addresses the challenge of accurate and efficient medical imaging analysis in healthcare,
aiming to reduce human error and workload for radiologists. The proposed solution involves developing advanced AI
models for Visual Question Answering (VQA) to assist healthcare professionals in analyzing
medical images (radiology images) quickly and accurately. We fine-tune HuggingFace multimodal model Idefics2-8b using radiology VQA datasets.
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<h3 class="overview-heading"><span class="vl">Dataset</span></h3>
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We fine-tune pre-trained model using these datasets :
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<ul>
<li><a href="https://huggingface.co/datasets/flaviagiammarino/vqa-rad" target="_blank">VQA-RAD dataset</a></li>
<li><a href="https://huggingface.co/datasets/mdwiratathya/SLAKE-vqa-english" target="_blank">SLAKE dataset</a></li>
<li><a href="https://huggingface.co/datasets/mdwiratathya/ROCO-radiology" target="_blank">ROCO dataset</a></li>
</ul>
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<h3 class="overview-heading"><span class="vl">Model Architecture</span></h3>
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<p class="overview-content">The model is trained using Idefics2-8b.</p>
<img class="content-image" src="https://raw.githubusercontent.com/Kalbe-x-Bangkit/C24-RM-Kalbe-Bangkit/main/img/idefics2_architecture.png" alt="model-architecture" />
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<h3 class="overview-heading"><span class="vl">Demo</span></h3>
<p class="overview-content">Please upload an image and question or select from the examples to see the answer prediction</p>
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