Semi-supervised COVID-19 CT image segmentation using deep generative models.

ABSTRACT:

Background

A recurring problem in image segmentation is a lack of labelled data. This problem is especially acute in the segmentation of lung computed tomography (CT) of patients with Coronavirus Disease 2019 (COVID-19). The reason for this is simple: the disease has not been prevalent long enough to generate a great number of labels. Semi-supervised learning promises a way to learn from data that is unlabelled and has seen tremendous advancements in recent years. However, due to the complexity of its label space, those advancements cannot be applied to image segmentation. That being said, it is this same complexity that makes it extremely expensive to obtain pixel-level labels, making semi-supervised learning all the more appealing. This study seeks to bridge this gap by proposing

SUBMITTER: Zammit J 

PROVIDER: S-EPMC9381397 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature