Evaluation of the Combined Deep Learning Networks Using Mask R-CNN and ResNet50 Classification for Detection and Diagnosis of Breast Cancer on MRI
Yang Zhang1,2, Yan-Lin Liu1, Ke Nie2, Jiejie Zhou3, Zhongwei Chen3, Jeon-Hor Chen1, Meihao Wang3, and Min-Ying Su1
1Department of Radiological Sciences, University of California, Irvine, CA, United States, 2Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States, 3Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
We developed two deep learning methods for breast MRI evaluation, first using Mask R-CNN for detection of suspicious areas, and then using ResNet50 for estimating the malignancy probability. These two networks were combined to test its diagnostic validity in two datasets. In Dataset-1, sensitivity=96.1% and specificity=78.1%. In Dataset-2, sensitivity=81.1% and specificity= 80.6%. We further characterized all false positives (FPs), and found other than confirmed benign lesions, FPs may come from vessels and asymmetric parenchymal enhancements, which can be further eliminated by other algorithms. The results suggest the potential of combined deep learning networks as a fully-automatic breast MRI CAD.
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