Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI.

ABSTRACT:

Background

The imaging features of focal liver lesions (FLLs) are diverse and complex. Diagnosing FLLs with imaging alone remains challenging. We developed and validated an interpretable deep learning model for the classification of seven categories of FLLs on multisequence MRI and compared the differential diagnosis between the proposed model and radiologists.

Methods

In all, 557 lesions examined by multisequence MRI were utilised in this retrospective study and divided into training-validation (n = 444) and test (n = 113) datasets. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the model. The accuracy and confusion matrix of the model and individual radiologists were compared. Saliency maps were generated to h

SUBMITTER: Wang SH 

PROVIDER: S-EPMC8613326 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature