3 citations · 4 across the 9 of their papers we have counts for
9 papers
Fundus-R1: Training a Fundus-Reading MLLM on Public Data
Yuchuan Deng, Qijie Wei, Kaiheng Qian +8
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…
Early Intervention for VFM-based Multimodal Medical Image Classification
Qijie Wei, Hailan Lin, Xirong Li
Current methods for multimodal medical image classification (M3IC) face two major challenges. First, the prevailing "fusion after unimodal image embedding" paradigm cannot fully ex…
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…
Supervised Domain Adaptation for Recognizing Retinal Diseases from Wide-Field Fundus Images
Qijie Wei, Jingyuan Yang, Bo Wang +9
This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. For an effective use of existing larg…
Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching
Jiazhen Liu, Xirong Li, Qijie Wei +2
For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel sem…
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…
Learn to Segment Retinal Lesions and Beyond
Qijie Wei, Xirong Li, Weihong Yu +8
Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a m…