Meta-learning-based retinal pathology classification from optical coherence tomography images

Ziting Yin; Xinjian Chen; Weifang Zhu; Dehui Xiang; Qing Peng; Fei Shi

doi:10.1117/12.2653478

3 April 2023 Meta-learning-based retinal pathology classification from optical coherence tomography images

Ziting Yin, Xinjian Chen, Weifang Zhu, Dehui Xiang, Qing Peng, Fei Shi

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124643R (2023) https://doi.org/10.1117/12.2653478
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

We propose to apply model-agnostic meta-learning (MAML) and MAML++ for pathology classification from optical coherence tomography (OCT) images. These meta-learning methods train a set of initialization parameters using training tasks, by which the model achieves fast convergence in new tasks with only a small amount of data. Our model is pretrained on an OCT dataset with seven types of retinal pathologies, and then refined and tested on another dataset with three types of pathologies. The classification accuracies of MAML and MAML++ reached 90.60% and 95.60% respectively, which are higher than the traditional deep learning method with pretraining.

Citation Download Citation

Ziting Yin, Xinjian Chen, Weifang Zhu, Dehui Xiang, Qing Peng, and Fei Shi "Meta-learning-based retinal pathology classification from optical coherence tomography images", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124643R (3 April 2023); https://doi.org/10.1117/12.2653478

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