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[2512.09069] KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification
Summary
The paper presents KD-OCT, a novel knowledge distillation framework that enhances the efficiency of deep learning models for classifying retinal OCT images, achieving high diagnostic performance with reduced computational demands.
Why It Matters
As age-related macular degeneration and related conditions are major causes of vision loss, efficient diagnostic tools are crucial. KD-OCT enables real-time deployment of advanced models in clinical settings, potentially improving patient outcomes and accessibility to eye care.
Key Takeaways
- KD-OCT compresses a high-performance teacher model into a lightweight student model.
- The method balances soft knowledge transfer and hard supervision for effective learning.
- KD-OCT achieves near-teacher performance while significantly reducing model size and inference time.
- The framework facilitates edge deployment for efficient AMD screening.
- Experimental results show KD-OCT outperforms existing OCT classifiers in efficiency-accuracy balance.
Computer Science > Computer Vision and Pattern Recognition arXiv:2512.09069 (cs) [Submitted on 9 Dec 2025 (v1), last revised 25 Feb 2026 (this version, v2)] Title:KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification Authors:Erfan Nourbakhsh, Nasrin Sanjari, Ali Nourbakhsh View a PDF of the paper titled KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification, by Erfan Nourbakhsh and 2 other authors View PDF HTML (experimental) Abstract: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 effectivenes...