Weakly-supervised explainable infection severity classification from chest CT scans.

ABSTRACT: Novel respiratory diseases can have a devastating impact on healthcare systems, which underlines the importance of developing methods to improve the prevention, diagnosis, and prognosis of such diseases. Developing computer-aided diagnosis tools that determine infection severity can aid healthcare professionals in deciding treatment strategies and preventing cross-infection. In such manner, lung infection severity classification from chest CTs is crucial for deciding treatment plans and interventions needed to block illness progression in individual patients and reduce cross infection. However, current techniques face performance, generalizability, and explainability challenges for automated infection severity classification methods due to the high spatial complexity of 3D volumes. Signifi

SUBMITTER: Almakky I 

PROVIDER: S-EPMC12574926 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature