Abstract MS1-2: Deep learning systems for reading mammograms and breast tomosynthesis
N. Karssemeijer · Cancer Research · 2019
Abstract Recent breakthroughs in machine learning resulted in powerful techniques that allow reseachers to accelerate development of automated systems for interpreting medical images. With the use of deep learning and very large training databases, limitations of conventional computer aided detection (CAD) systems, which are widely used in mammography, can be overcome. It is expected that in the coming years algorithms will become more accurate that human readers for interpreting screening mammograms. This will enable physicians to substantially increase quality of mammography. It also will reduce the need for human resources, which is important because many countries face challenges with recruitment of mammographers. In the presentation, experimental study results will be presented which illustrate that deep learning systems are approaching the level of performance of human readers and that there is potential to increase the quality of automated interpretation of mammograms and breast tomosynthesis beyond to the level of experts Citation Format: Karssemeijer N. Deep learning systems for reading mammograms and breast tomosynthesis [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr MS1-2.