arXiv:2609.21164v1 Announce Type: new
Abstract: Integrating diverse data modalities --- such as clinical notes, laboratory results, and medical imaging --- is essential for advancing clinical decisio...
By Inyoung Choi, Sukwon Yun, Jiayi Xin, Jie Peng, Tianlong Chen, Qi Long
arXiv:2605. 22547v3 Announce Type: replace-cross Abstract: Medical image diagnosis has achieved significant progress with deep learning, yet existing methods often rely on isolated visual evidence and lack the ability to effectively leverage similar cases and external knowledge.
By Yiming Xu, Yixuan Liu, Yuhang Zhang, Ling Zheng, Yihan Wang, Qi Song
arXiv:2606. 26458v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over knowledge graphs has emerged as a promising approach for grounding large language models, yet existing benchmarks largely overlook the challenges of retrieval in multimodal knowledge graph RAG (MKG-RAG).
By Xiaochen Wang, Bao Hoang, Han Liu, Ting Wang, Fenglong Ma
arXiv:2610.33691v1 Announce Type: cross
Abstract: Whole Slide Image (WSI) analysis serves as the fundamental method for the clinical diagnosis of cancer in digital histopathology. Recent advances in...
By Nadun Rajapaksha, Sandun Herath, Devnith Wijesinghe, Isiri Amani Withanawasam, Hasindri Watawana, Ranga Rodrigo
The paper introduces a model‑agnostic framework that lets frozen large language models (LLMs) and vision‑language models (VLMs) learn from deployment experience without retraining. It does so by adding three external expertise components—a Skill for guiding reasoning and tool use, a Knowledge Memory for storing reliable facts, and a Multimodal Knowledge Base for visual examples—and by using a validation strategy that updates only when new cases improve performance without harming past performance. Experiments on six benchmarks with both open‑weight and closed‑source models show up to a 34.2% performance gain on medical tasks, generalization to unseen cases, and transferability to other models and non‑medical domains.
By Yexiao He, Yucheng Tang, Pengfei Guo, Yufan He, Andriy Myronenko, Can Zhao, Ang Li, Daguang Xu, Dong Yang
The paper introduces MedForget, a hierarchy‑aware multimodal unlearning benchmark that models medical data as nested institutional structures, allowing fine‑grained evaluation of retain and forget splits. Experiments reveal that existing unlearning methods struggle to achieve effective hierarchy‑aware forgetting without harming retained utility on clinically relevant tasks. To address this, the authors propose CHIP, a training‑free method that deletes target‑specific weight subspaces while preserving sibling‑shared information, achieving superior forget‑retain performance across all hierarchy levels while maintaining competitive downstream utility.
By Fengli Wu, Vaidehi Patil, Jaehong Yoon, Yue Zhang, Mohit Bansal