6 papers
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…
DB-KAUNet: An Adaptive Dual Branch Kolmogorov-Arnold UNet for Retinal Vessel Segmentation
Hongyu Xu, Panpan Meng, Meng Wang +3
Accurate segmentation of retinal vessels is crucial for the clinical diagnosis of numerous ophthalmic and systemic diseases. However, traditional Convolutional Neural Network (CNN)…
Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?
Qingshan Hou, Yukun Zhou, Jocelyn Hui Lin Goh +19
The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.…
FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis
Ke Zou, Jocelyn Hui Lin Goh, Yukun Zhou +11
Foundation models (FMs) have shown great promise in medical image analysis by improving generalization across diverse downstream tasks. In ophthalmology, several FMs have recently…
A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers
Meng Wang, Tian Lin, Qingshan Hou +37
Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings,…
Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model
Meng Wang, Tian Lin, Aidi Lin +46
Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce a knowledge-rich vision-language model (RetiZero…