Deep Learning in Mammography Breast Cancer Detection

Richa Agarwal, Moi Hoon Yap, Md Kamrul Hasan, Reyer Zwiggelaar, Robert Marti

Research output: Chapter in Book/Report/Conference proceedingChapter

29 Downloads (Pure)

Abstract

Breast cancer incidence has increased in the past decades. Extensive efforts are being made for early detection to reduce the mortality rate. As one of the leading field in artificial intelligence, deep learning algorithms have been widely used in breast cancer research in recent years, ranging from detection, segmentation to classification. To provide insights and development in this field, we review and summarize the popular datasets and deep learning methods used in breast cancer detection, focusing on mammography. We provide a summary of the state-of-the-art deep learning methods in mammography breast cancer detection and its performance. We discuss the challenges in breast cancer detection and provide some new insights to advance the field.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine
EditorsNiklas Lidströmer, Hutan Ashrafian
PublisherSpringer Nature
Pages1287-1300
Number of pages14
ISBN (Electronic)9783030645731
ISBN (Print)9783030645724
DOIs
Publication statusPublished - 18 Feb 2022

Publication series

NameArtificial Intelligence in Medicine
PublisherElsevier
ISSN (Print)0933-3657

Fingerprint

Dive into the research topics of 'Deep Learning in Mammography Breast Cancer Detection'. Together they form a unique fingerprint.

Cite this