基于眼底增强的疾病感知蒸馏模型的OCT图像视网膜疾病分类
Fundus-Enhanced Disease-Aware Distillation Model for Retinal Disease Classification from OCT Images
AI总结：
针对OCT与眼底图像多模态框架需配对数据不切实际的问题，提出眼底增强疾病感知蒸馏模型（FDDM），通过类别原型匹配和相似性对齐在训练时将眼底模型信息蒸馏至OCT模型，测试时无需眼底图像，有效提升了视网膜疾病分类性能与临床实用性。
摘要：
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。由于收集OCT图像比眼底照片相对更昂贵，现有方法使用多模态学习，用眼底图像的额外上下文来补充有限的OCT数据。然而，多模态框架需要两种模态的眼部配对数据集，这在临床使用中是不切实际的。为了解决这个问题，我们提出了一种新颖的基于眼底增强的疾病感知蒸馏模型（FDDM），用于从OCT图像中进行视网膜疾病分类。我们的框架在训练期间通过利用未配对的眼底图像来增强OCT模型，并且在测试期间不需要使用眼底图像，这极大地提高了我们方法在临床使用中的实用性和效率。具体来说，我们提出了一种新颖的类别原型匹配，将疾病相关信息从眼底模型蒸馏到OCT模型中，并提出了一种新颖的类别相似性对齐，以强制两种模态的疾病分布之间的一致性。实验结果表明，我们提出的方法在视网膜疾病分类方面优于单模态、多模态和最先进的蒸馏方法。代码可在 https://github.com/xmed-lab/FDDM 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
