Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning  

作　　者：ZHAO Qi MAI Si Wei LI Qian HUANG Guan Chong GAO Ming Chen YANG Wen Li WANG Ge MA Ya LI Lei PENG Xiao Yan 

机构地区：[1]Department of Ophthalmology,Beijing Tongren Eye Center,Beijing Tongren Hospital,Capital Medical University,Beijing Key Laboratory of Ophthalmology and Visual Sciences,Beijing 100730,China [2]Department of Computer Science,Rutgers,The State University of New Jersey,New Brunswick 08901,USA [3]Department of Computer Science and Engineering,University at Buffalo,Buffalo 14260,USA [4]Beijing Institute of Ophthalmology,Beijing Tongren Hospital,Capital Medical University,Beijing Ophthalmology and Visual Science KeyLaboratory,Beijing 100730,China

出　　处：《Biomedical and Environmental Sciences》2023年第5期431-440,共10页生物医学与环境科学（英文版）

基　　金：supported by National Natural Science Foundation of China [No.82171073]。

摘　　要：Objective To develop a few-shot learning(FSL) approach for classifying optical coherence tomography(OCT) images in patients with inherited retinal disorders(IRDs).Methods In this study, an FSL model based on a student–teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control samples.Results The FSL model achieved a total accuracy of 0.974–0.983, total sensitivity of 0.934–0.957, total specificity of 0.984–0.990, and total F1 score of 0.935–0.957, which were superior to the total accuracy of the baseline model of 0.943–0.954, total sensitivity of 0.866–0.886, total specificity of 0.962–0.971,and total F1 score of 0.859–0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves(AUC) of the receiver operating characteristic(ROC) curves in most subclassifications.Conclusion This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.

关 键 词：Few-shot learning Student-teacher learning Knowledge distillation Transfer learning Optical coherence tomography Retinal degeneration Inherited retinal diseases 

分 类 号：R774.1[医药卫生—眼科]