Privacy-preserving continual learning methods for medical image classification: a comparative analysis.

ABSTRACT:

Background

The implementation of deep learning models for medical image classification poses significant challenges, including gradual performance degradation and limited adaptability to new diseases. However, frequent retraining of models is unfeasible and raises concerns about healthcare privacy due to the retention of prior patient data. To address these issues, this study investigated privacy-preserving continual learning methods as an alternative solution.

Methods

We evaluated twelve privacy-preserving non-storage continual learning algorithms based deep learning models for classifying retinal diseases from public optical coherence tomography (OCT) images, in a class-incremental learning scenario. The OCT dataset comprises 108,309 OCT images. Its classes include normal

SUBMITTER: Verma T 

PROVIDER: S-EPMC10461441 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature