Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network

T. de Moor, A. Rodriguez-Ruiz, R. Mann and J. Teuwen

in: International Workshop on Breast Imaging, 2018

Abstract

Computer-aided detection or decision support systems aim to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. Commonly such methods proceed in two steps: selection of candidate regions for malignancy, and later classification as either malignant or not. In this study, we present a candidate detection method based on deep learning to automatically detect and additionally segment soft tissue lesions in DM. A database of DM exams (mostly bilateral and two views) was collected from our institutional archive.

A pdf file of this publication is available for personal use. Enter your e-mail address in the box below and press the button. You will receive an e-mail message with a link to the pdf file.