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A Multi-modal Interaction Enhanced Segment Anything Model (MIE-SAM) that reconfigures SAM's image encoder into a weight-sharing dual-branch image encoders, and translates the fused features into the fine-grained saliency map in an entirely prompt-free, end-to-end manner.
Large language models (LLMs) have demonstrated strong capabilities across diverse domains, showing considerable potential in medicine. However, their application in medical settings remains limited by the scarcity of visual question answering (VQA) datasets that capture clinical reasoning and explicit image-text alignm...
A FOundational LLM Trained on ThoughtMed-1M (FOLTMed), a scalable paradigm for advancing research on clinically grounded multimodal LLMs, achieved state-of-the-art performance across 42 medical VQA benchmark datasets, with a macro accuracy of 85.4%, and generated more clinically coherent responses on the ThoughtMed-1M...
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