Cross-Architecture Knowledge Distillation (KD) for Retinal Fundus Image Anomaly Detection on NVIDIA Jetson Nano
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arXiv
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| Format: | Preprint |
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2025
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| author | Yilmaz, Berk Aiyengar, Aniruddh |
| author_facet | Yilmaz, Berk Aiyengar, Aniruddh |
| contents | Early and accurate identification of retinal ailments is crucial for averting ocular decline; however, access to dependable diagnostic devices is not often available in low-resourced settings. This project proposes to solve that by developing a lightweight, edge-device deployable disease classifier using cross-architecture knowledge distilling. We first train a high-capacity vision transformer (ViT) teacher model, pre-trained using I-JEPA self-supervised learning, to classify fundus images into four classes: Normal, Diabetic Retinopathy, Glaucoma, and Cataract. We kept an Internet of Things (IoT) focus when compressing to a CNN-based student model for deployment in resource-limited conditions, such as the NVIDIA Jetson Nano. This was accomplished using a novel framework which included a Partitioned Cross-Attention (PCA) projector, a Group-Wise Linear (GL) projector, and a multi-view robust training method. The teacher model has 97.4 percent more parameters than the student model, with it achieving 89 percent classification with a roughly 93 percent retention of the teacher model's diagnostic performance. The retention of clinical classification behavior supports our method's initial aim: compression of the ViT while retaining accuracy. Our work serves as an example of a scalable, AI-driven triage solution for retinal disorders in under-resourced areas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18220 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Architecture Knowledge Distillation (KD) for Retinal Fundus Image Anomaly Detection on NVIDIA Jetson Nano Yilmaz, Berk Aiyengar, Aniruddh Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 68T07 I.2.6; I.5.1; J.3 Early and accurate identification of retinal ailments is crucial for averting ocular decline; however, access to dependable diagnostic devices is not often available in low-resourced settings. This project proposes to solve that by developing a lightweight, edge-device deployable disease classifier using cross-architecture knowledge distilling. We first train a high-capacity vision transformer (ViT) teacher model, pre-trained using I-JEPA self-supervised learning, to classify fundus images into four classes: Normal, Diabetic Retinopathy, Glaucoma, and Cataract. We kept an Internet of Things (IoT) focus when compressing to a CNN-based student model for deployment in resource-limited conditions, such as the NVIDIA Jetson Nano. This was accomplished using a novel framework which included a Partitioned Cross-Attention (PCA) projector, a Group-Wise Linear (GL) projector, and a multi-view robust training method. The teacher model has 97.4 percent more parameters than the student model, with it achieving 89 percent classification with a roughly 93 percent retention of the teacher model's diagnostic performance. The retention of clinical classification behavior supports our method's initial aim: compression of the ViT while retaining accuracy. Our work serves as an example of a scalable, AI-driven triage solution for retinal disorders in under-resourced areas. |
| title | Cross-Architecture Knowledge Distillation (KD) for Retinal Fundus Image Anomaly Detection on NVIDIA Jetson Nano |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 68T07 I.2.6; I.5.1; J.3 |
| url | https://arxiv.org/abs/2506.18220 |