  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.
