Visual Interpretability in Computer-Assisted Diagnosis of Thyroid Nodules Using Ultrasound Images.

ABSTRACT: BACKGROUND The number of studies on deep learning in artificial intelligence (AI)-assisted diagnosis of thyroid nodules is increasing. However, it is difficult to explain what the models actually learn in artificial intelligence-assisted medical research. Our aim is to investigate the visual interpretability of the computer-assisted diagnosis of malignant and benign thyroid nodules using ultrasound images. MATERIAL AND METHODS We designed and implemented 2 experiments to test whether our proposed model learned to interpret the ultrasound features used by ultrasound experts to diagnose thyroid nodules. First, in an anteroposterior/transverse (A/T) ratio experiment, multiple models were trained by changing the A/T ratio of the original nodules, and their classification, accuracy, sensitivity

SUBMITTER: Wei X 

PROVIDER: S-EPMC7446277 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature