Screening Mammogram Classification with Prior Exams

Medical Imaging with Deep Learning 2019 MIDL 2019 - Extended Abstract Track Screening Mammogram Classification with Prior Exams Jungkyu Park 1, Jason Phang 1, Yiqiu Shen 1, Nan Wu 1, S. Gene Kim 2, Linda Moy 2, Kyunghyun Cho 1, Krzysztof J. Geras 2, 1 1 Center for Data Science, New York University 2 Department of Radiology, New York University School of Medicine 1. Introduction Screening mammography had been shown to significantly reduce the mortality rate for breast cancer (Kopans, 2002; Duffy et al., 2002a,b), the second leading cause of cancer-related deaths among women in the United States. However, there is a high rate of false positive recalls and biopsies associated with breast cancer screening. Among the 10-15% of women asked for recall, only 10-20% within that subset are recommended for biopsy. Among those biopsies, only 20-40% are diagnosed with cancer (Kopans, 2015). Given the success of deep learning in computer vision, many deep neural network models have been applied to breast cancer screening (Ribli et al., 2018; Lotter et al., 2017; Geras et al., 2017; Wu et al., 2018, 2019a).