Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study.

ABSTRACT:

Background

Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow.

Methods

A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module

SUBMITTER: Zheng T 

PROVIDER: S-EPMC10034267 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature