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Although Vision Language Models (VLMs) have shown strong generalization in medical imaging, pathology presents unique challenges due to ultra-high resolution, complex tissue structures, and nuanced clinical semantics.
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Y. Sun, H. Wu, C. Zhu, S. Zheng, Q. Chen, K. Zhang, Y. Zhang, D. Wan, X. Lan, M. Zheng et al. , “Pathmmu: A massive multimodal expert-level benchmark for understanding and reasoning in pathology,” in European Conference on Computer Vision (ECCV) , pp. 56–73, 2024
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P. Liu, L. Ji, J. Gou, B. Fu, and M. Ye, “Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology,” in The Thirteenth International Conference on Learning Representations (ICLR) , 2025
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P. Xia, K. Zhu, H. Li, T. Wang, W. Shi, S. Wang, L. Zhang, J. Zou, and H. Yao, “MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models,” in The Thirteenth International Conference on Learning Representations (ICLR) , 2025
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