2023/08/01 by Lehan Wang, Wang, Lehan, Weihang Dai +7 · 3 citations
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Retinal Imaging and Analysis #Retinal and Optic Conditions #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2308.00291
openalex publication_date 2023/08/01 · openalex created_date 2023/08/18 · openalex updated_date 2026/07/28
Optical Coherence Tomography (OCT) is a novel and effective screening tool for ophthalmic examination. Since collecting OCT images is relatively more expensive than fundus photographs, existing methods use multi-modal learning to complement limited OCT data with additional context from fundus images. However, the multi-modal framework requires eye-paired datasets of both modalities, which is impractical for clinical use. To address this problem, we propose a novel fundus-enhanced disease-aware distillation model (FDDM), for retinal disease classification from OCT images. Our framework enhances the OCT model during training by utilizing unpaired fundus images and does not require the use of fundus images during testing, which greatly improves the practicality and efficiency of our method for clinical use. Specifically, we propose a novel class prototype matching to distill disease-related information from the fundus model to the OCT model and a novel class similarity alignment to enforce consistency between disease distribution of both modalities. Experimental results show that our proposed approach outperforms single-modal, multi-modal, and state-of-the-art distillation methods for retinal disease classification. Code is available at https://github.com/xmed-lab/FDDM.