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20242026
most citedSTLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering

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cs.CV2026

PlanPath: Exploration-Guided Orchestration for Patient-Level Pathology Reasoning

Jiusong Ge, Mingzhao He, Qidong Liu +8

A patient's pathology case spans tens of slides from distinct specimens and anatomical sites, each carrying non-redundant, site-specific evidence. Forming a patient-level conclusio…

cs.CV2026

Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence

Xingyue Wang, Bo Liu, Meng Wang +4

Visual Question Answering (VQA) holds great promise for clinical support, particularly in ophthalmology, where retinal fundus photography is essential for diagnosis. However, ophth…

cs.CV2026

Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning

Jiusong Ge, Yingkang Zhan, Wenjie Zhao +13

Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and…

cs.CV2026

Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts

Yifan Gao, Tao Zhou, Yi Zhou +3

Medical Visual Grounding (MVG) aims to identify diagnostically relevant phrases from free-text radiology reports and localize their corresponding regions in medical images, providi…

cs.CV2026

Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge

Shuai Lu, Meng Wang, Jia Guo +6

Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis. However, their clinical deployment is severely hindered by lacking domain-specific k…

cs.CV2025

Colon-X: Advancing Intelligent Colonoscopy toward Clinical Reasoning

Ge-Peng Ji, Jingyi Liu, Deng-Ping Fan +2

In this study, we present Colon-X, an open initiative aimed at advancing multimodal intelligence in colonoscopy. We begin by constructing ColonVQA, the most comprehensive multimoda…

cs.CV2024★ 1 cited

STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering

Guohao Sun, Can Qin, Huazhu Fu +2

Large Vision-Language Models (LVLMs) have shown significant potential in assisting medical diagnosis by leveraging extensive biomedical datasets. However, the advancement of medica…