Yiming Gao1, Krzysztof J Geras2, Alana A Lewin1, Linda Moy1,3. 1. 1 Department of Radiology, New York University School of Medicine, 160 E 34th St, New York, NY 10016. 2. 2 Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY. 3. 3 Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY.
Abstract
OBJECTIVE: The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future. CONCLUSION: CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.
OBJECTIVE: The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future. CONCLUSION: CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.
Authors: Yiqiu Shen; Nan Wu; Jason Phang; Jungkyu Park; Kangning Liu; Sudarshini Tyagi; Laura Heacock; S Gene Kim; Linda Moy; Kyunghyun Cho; Krzysztof J Geras Journal: Med Image Anal Date: 2020-12-16 Impact factor: 8.545
Authors: Ziba Gandomkar; Somphone Siviengphanom; Ernest U Ekpo; Mo'ayyad Suleiman; Seyedamir Tavakoli Taba; Tong Li; Dong Xu; Karla K Evans; Sarah J Lewis; Jeremy M Wolfe; Patrick C Brennan Journal: Sci Rep Date: 2021-10-11 Impact factor: 4.379