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20222026
most citedFederated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging

12 citations · 32 across the 15 of their papers we have counts for

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7 papers · 1 filter

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

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

Vision-Language Model IP Protection via Prompt-based Learning

Lianyu Wang, Meng Wang, Huazhu Fu +1

Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard…

cs.CV2024★ 3 cited

MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM

Nan Zhou, Ke Zou, Kai Ren +7

The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to…

cs.CV2024★ 2 cited

Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding

Ke Zou, Yang Bai, Bo Liu +9

Medical phrase grounding is crucial for identifying relevant regions in medical images based on phrase queries, facilitating accurate image analysis and diagnosis. However, current…

cs.CV2023

A Multi-view Impartial Decision Network for Frontotemporal Dementia Diagnosis

Guoyao Deng, Ke Zou, Meng Wang +3

Frontotemporal Dementia (FTD) diagnosis has been successfully progress using deep learning techniques. However, current FTD identification methods suffer from two limitations. Firs…

cs.CV2023★ 1 cited

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

Kai Ren, Ke Zou, Xianjie Liu +5

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual…