Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study.

ABSTRACT:

Background

This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM.

Methods

Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, a

SUBMITTER: Mao N 

PROVIDER: S-EPMC9977865 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature