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

老年性黃斑部病變（AMD）與脈絡膜新生血管（CNV）相關疾病是全球視力喪失的主因，而光學相干斷層掃描（OCT）已成為早期檢測與管理的關鍵技術。然而，ConvNeXtV2-Large等尖端深度學習模型因計算需求龐大，難以在臨床環境中部署。因此，亟需開發能維持高診斷效能且可即時運作的高效模型。本研究提出新型知識蒸餾框架KD-OCT，透過先進數據增強、隨機權重平均與焦點損失強化ConvNeXtV2-Large教師模型，將其壓縮為輕量型EfficientNet-B2學生模型，用於分類正常、玻璃膜疴及CNV病例。KD-OCT採用即時蒸餾技術，結合軟性教師知識轉移與硬性真實標註的混合損失函數。在諾爾眼科醫院（NEH）數據集上以患者級別交叉驗證評估顯示，KD-OCT在效率-準確度平衡性上超越多尺度與特徵融合OCT分類器，以大幅縮減的模型體積與推論時間達成接近教師模型的效能。儘管經過壓縮，學生模型仍優於多數現有框架，為AMD篩檢的邊緣部署提供可行性。程式碼公開於：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.