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.

翻译：光学相干断层扫描（OCT）是一种新型且有效的眼科检查筛查工具。由于采集OCT图像的成本相对高于眼底照片，现有方法利用多模态学习，通过眼底图像的额外上下文信息来补充有限的OCT数据。然而，多模态框架需要同时包含两种模态的眼部配对数据集，这在临床应用中并不现实。为解决这一问题，我们提出了一种新颖的眼底增强疾病感知蒸馏模型（FDDM），用于基于OCT图像的视网膜疾病分类。我们的框架在训练阶段利用非配对的眼底图像增强OCT模型，并在测试阶段无需使用眼底图像，从而显著提升了方法在临床使用中的实用性和效率。具体而言，我们提出了新颖的类别原型匹配机制，用于从眼底模型向OCT模型蒸馏疾病相关信息，以及新颖的类别相似性对齐方法，以强制两种模态的疾病分布保持一致。实验结果表明，我们提出的方法在视网膜疾病分类任务上优于单模态、多模态及当前最先进的蒸馏方法。代码已开源：https://github.com/xmed-lab/FDDM。