Self-supervised pre-training improves fundus image classification for diabetic retinopathy

Joohyung Lee; Eung-Joo Lee

doi:10.1117/12.2632901

27 May 2022 Self-supervised pre-training improves fundus image classification for diabetic retinopathy

Joohyung Lee, Eung-Joo Lee

Proceedings Volume 12102, Real-Time Image Processing and Deep Learning 2022; 121020L (2022) https://doi.org/10.1117/12.2632901
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

This paper assesses the efficacy of self-supervised learning in the DeepDR Diabetic Retinopathy Image Dataset (DeepDRiD). Recently, self-supervised learning has achieved great success in the field of Computer Vision. Particularly, self-supervised learning can effectively serve the field of medical imaging where a large amount of labeled data is usually limited. In this paper, we apply the Bootstrap Your Own Latent (BYOL) approach to grade diabetic retinopathy which scores the lowest among the MedMNIST dataset. With the pre-trained model using BYOL, we evaluate the efficacy of the BYOL approach on DeepDRiD following fine-tuning protocols. Further, we compare the performance of the model with the model from scratch and proved the effectiveness of BYOL in DeepDRiD. Our experiment shows that BYOL can boost the performance of grading diabetic retinopathy.

Citation Download Citation

Joohyung Lee and Eung-Joo Lee "Self-supervised pre-training improves fundus image classification for diabetic retinopathy", Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 121020L (27 May 2022); https://doi.org/10.1117/12.2632901

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