Lung cancer diagnosis using deep attention-based multiple instance learning and radiomics.

ABSTRACT:

Background

Early diagnosis of lung cancer is a key intervention for the treatment of lung cancer in which computer-aided diagnosis (CAD) can play a crucial role. Most published CAD methods perform lung cancer diagnosis by classifying each lung nodule in isolation. However, this does not reflect clinical practice, where clinicians diagnose a patient based on a set of images of nodules, instead of looking at one nodule at a time. Besides, the low interpretability of the output provided by these methods presents an important barrier for their adoption.

Method

In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem, which better reflects the diagnosis process in the clinical setting and provides higher interpretability of the output. We sele

SUBMITTER: Chen J 

PROVIDER: S-EPMC9310706 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature