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Abstract #1120

Application of Two Deep Learning Networks for Diagnosis of Breast Cancer on MRI: Automatic Detection Using Mask R-CNN Followed by Classification Using ResNet50

Yang Zhang1,2, Yan-Lin Liu2, Ke Nie1, Jiejie Zhou3, Siwa Chan4, Vivian Youngjean Park5, Min Jung Kim5, Zhongwei Chen3, Jeon-Hor Chen2,4, Meihao Wang3, and Min-Ying Su2
1Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States, 2Department of Radiological Sciences, University of California, Irvine, CA, United States, 3Department of Radiology, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, 4Department of Medical Imaging, Taichung Tzu-Chi Hospital, Taichung, Taiwan, 5Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of

Mask R-CNN and ResNet50 were implemented to search the entire breast MRI to identify suspicious lesions, and then to further evaluate their likelihood of malignancy. The dataset included 103 malignant and 73 benign lesions in 153 patients. In detection phase using Mask R-CNN, 101 malignant, 48 benign, and 130 normal enhancing tissues were detected as suspicious. When putting them into ResNet50 for characterization, 99 cancers were correctly diagnosed as malignant, and only 16 benign lesions and 16 normal enhancing tissues remained as likely malignant. The true positive rate was 99/103=96%, and many detected false positives were dismissed during classification phase.

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