6 papers
Fundus-R1: Training a Fundus-Reading MLLM with Knowledge-Aware Reasoning on Public Data
Yuchuan Deng, Qijie Wei, Kaiheng Qian +6
Fundus imaging such as CFP, OCT and UWF is crucial for the early detection of retinal anomalies and diseases. Fundus image understanding, due to its knowledge-intensive nature, pos…
EI: Early Intervention for Multimodal Imaging based Disease Recognition
Qijie Wei, Hailan Lin, Xirong Li
Current methods for multimodal medical imaging based disease recognition face two major challenges. First, the prevailing "fusion after unimodal image embedding" paradigm cannot fu…
Co-Teaching for Unsupervised Domain Adaptation and Expansion
Hailan Lin, Qijie Wei, Kaibin Tian +2
Unsupervised Domain Adaptation (UDA) essentially trades a model's performance on a source domain for improving its performance on a target domain. To overcome this, Unsupervised Do…
Cross-modal Fundus Image Registration under Large FoV Disparity
Hongyang Li, Junyi Tao, Qijie Wei +4
Previous work on cross-modal fundus image registration (CMFIR) assumes small cross-modal Field-of-View (FoV) disparity. By contrast, this paper is targeted at a more challenging sc…
FunBench: Benchmarking Fundus Reading Skills of MLLMs
Qijie Wei, Kaiheng Qian, Xirong Li
Multimodal Large Language Models (MLLMs) have shown significant potential in medical image analysis. However, their capabilities in interpreting fundus images, a critical skill for…
Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation
Qijie Wei, Weihong Yu, Xirong Li
Previous research on retinal vessel segmentation is targeted at a specific image domain, mostly color fundus photography (CFP). In this paper we make a brave attempt to attack a mo…