A novel multimodal RAG framework tailored for MedVQA is proposed, which leverages multimodal data, including medical images, reports, and generated captions, to provide more accurate clinical answers, and introduces a training paradigm that uses captions as auxiliary supervision, enhancing cross-modal alignment via contrastive learning.
Dynamic Evidence-Guided Preference Optimization (DEPO) is proposed, a new framework that enables evidence-aware and adaptive preference learning for Med-LVLMs and introduces Multi-Modal Evidence Perturbation (MEP) to suppress non-causal textual and visual shortcuts and Dispre-ferred Evidence Resampling (DER) to continu...
Zixuan Huang, Zhihong Zhu, Xiaolong Liu et al.· Annual Meeting of the Associ...· 0 citations
A definitive taxonomy of the medical VLM landscape is provided, tracing the evolution from early Contrastive Alignment and Generative MLLMs to the cutting-edge frontiers of Dense Pixel-Grounding, Sparse Mixture-of-Experts (MoE), and Reasoning-Incentivized (RL) architectures.
Taha Razzaq, Murtaza Taj, Asim Iqbal· Journal of Biomedical Inform...· 0 citations
The proposed framework offers a practical and scalable approach to mitigating hallucinations without requiring task-specific fine-tuning, highlighting the potential of retrieval-augmented approaches for trustworthy artificial intelligence (AI)-assisted healthcare applications.
This work proposes MedVCoT, which incorporates latent visual reasoning into the medical visual question answering (VQA) domain, and utilizes the specialized expertise of MedSAM to train a large vision-language model so that it can autonomously generate consistent and continuous latent visual tokens within Visual Chain-...
Bo Xu, Quan-Hao Zhu, Bo-Lin Zhu et al.· Proceedings of the Thirty-Fi...· 1 citation
LocAnyMed-CoT-20K is derived, a rationale-augmented subset that connects anatomical context, visual observations, and spatial conclusions through structured reasoning and further improves cross-source generalization through fine-tuning.
Zi-Han Wang, Tong Liu, Zhi-Wei Wang et al.· 0 citations
A framework that strengthens clinical alignment by separating and enhancing training-time alignment and inference-time alignment is proposed and experiments show that complementary visual representations with a multi-encoder design and concept-level auxiliary learning help preserve clinically meaningful information.
Yunseo Lee, Hyun Jun Kim, Heeseung Shin et al.· 0 citations
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