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 Zhang^1,2, Yan-Lin Liu², Ke Nie¹, Jiejie Zhou³, Siwa Chan⁴, Vivian Youngjean Park⁵, Min Jung Kim⁵, Zhongwei Chen³, Jeon-Hor Chen^2,4, 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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