| Literature DB >> 33291266 |
Tomoyuki Fujioka1, Mio Mori1, Kazunori Kubota1,2, Jun Oyama1, Emi Yamaga1, Yuka Yashima1, Leona Katsuta1, Kyoko Nomura1, Miyako Nara1,3, Goshi Oda4, Tsuyoshi Nakagawa4, Yoshio Kitazume1, Ukihide Tateishi1.
Abstract
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women's health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.Entities:
Keywords: artificial intelligence; breast; deep learning; machine learning; neural network; ultrasound
Year: 2020 PMID: 33291266 PMCID: PMC7762151 DOI: 10.3390/diagnostics10121055
Source DB: PubMed Journal: Diagnostics (Basel) ISSN: 2075-4418