KD-OCT：面向临床级视网膜OCT分类的高效知识蒸馏方法

KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification

December 9, 2025

作者: Erfan Nourbakhsh, Nasrin Sanjari, Ali Nourbakhsh

cs.AI

摘要

年龄相关性黄斑变性（AMD）及其引发的脉络膜新生血管（CNV）相关疾病是全球范围内视力丧失的主要原因，而光学相干断层扫描（OCT）是早期发现与管理的核心技术。然而，ConvNeXtV2-Large等前沿深度学习模型因计算需求过高难以在临床部署。为此，亟需开发既能保持高诊断性能又可实现实时部署的高效模型。本研究提出新型知识蒸馏框架KD-OCT，通过先进数据增强、随机权重平均和焦点损失增强的ConvNeXtV2-Large教师模型，压缩为轻量级EfficientNet-B2学生模型，用于正常、玻璃膜疴和CNV病例分类。该框架采用实时蒸馏策略，通过结合软教师知识迁移与硬真值监督的混合损失函数实现平衡优化。在诺尔眼科医院数据集上进行的患者级交叉验证表明，KD-OCT在效率-准确率平衡性上优于同类多尺度或特征融合OCT分类器，以显著缩减的模型体积和推理时间达到接近教师模型的性能。尽管经过压缩，学生模型仍超越多数现有框架，为AMD筛查的边缘部署提供了可行性。代码详见https://github.com/erfan-nourbakhsh/KD-OCT。

English

Age-related macular degeneration (AMD) and choroidal neovascularization (CNV)-related conditions are leading causes of vision loss worldwide, with optical coherence tomography (OCT) serving as a cornerstone for early detection and management. However, deploying state-of-the-art deep learning models like ConvNeXtV2-Large in clinical settings is hindered by their computational demands. Therefore, it is desirable to develop efficient models that maintain high diagnostic performance while enabling real-time deployment. In this study, a novel knowledge distillation framework, termed KD-OCT, is proposed to compress a high-performance ConvNeXtV2-Large teacher model, enhanced with advanced augmentations, stochastic weight averaging, and focal loss, into a lightweight EfficientNet-B2 student for classifying normal, drusen, and CNV cases. KD-OCT employs real-time distillation with a combined loss balancing soft teacher knowledge transfer and hard ground-truth supervision. The effectiveness of the proposed method is evaluated on the Noor Eye Hospital (NEH) dataset using patient-level cross-validation. Experimental results demonstrate that KD-OCT outperforms comparable multi-scale or feature-fusion OCT classifiers in efficiency- accuracy balance, achieving near-teacher performance with substantial reductions in model size and inference time. Despite the compression, the student model exceeds most existing frameworks, facilitating edge deployment for AMD screening. Code is available at https://github.com/erfan-nourbakhsh/KD- OCT.