Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images.

ABSTRACT: Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally. Detecting infected individuals and analyzing their status can provide patients with proper healthcare while protecting the normal population. Chest CT (computed tomography) is an effective tool for screening of COVID-19. It displays detailed pathology-related information. To achieve automated COVID-19 diagnosis and lung CT image segmentation, convolutional neural networks (CNNs) have become mainstream methods. However, most of the previous works consider automated diagnosis and image segmentation as two independent tasks, in which some focus on lung fields segmentation and the others focus on single-lesion segmentation. Moreover, lack of clinical explainability is a common problem for CNN-based methods. In such c

SUBMITTER: Li M 

PROVIDER: S-EPMC9235304 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature