Learning to Discover Explainable Clinical Features With Minimum Supervision.

ABSTRACT:

Purpose

To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain.

Methods

Transfer learning with EfficientNet-B4 and semisupervised learning with SimCLR are used in this work. The largest public OCT dataset, consisting of 108,312 images and four categories (choroidal neovascularization, diabetic macular edema, drusen, and normal) is used. In addition, two smaller datasets are constructed, containing 31,200 images for the limited version and 4000 for the mini version of the dataset. To illustrate the effectiveness of the developed models, local interpretable model-agnostic explanations and class activation maps are used as explainability techniques.

Re

SUBMITTER: Al Turk L 

PROVIDER: S-EPMC8762682 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature