Fully automatic tumor segmentation of breast ultrasound images with deep learning.

ABSTRACT:

Background

Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis systems. While the majority of clinical BUS scans are normal ones without tumors, segmentation approaches such as U-Net often predict mass regions for these images. Such false-positive problem becomes serious if a fully automatic artificial intelligence system is used for routine screening.

Methods

In this study, we proposed a novel model which is more suitable for routine BUS screening. The model contains a classification branch that determines whether the image is normal or with tumors, and a segmentation branch that outlines t

SUBMITTER: Zhang S 

PROVIDER: S-EPMC9859996 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature