Abstract #4013

Evaluation of the Combined Deep Learning Networks Using Mask R-CNN and ResNet50 Classification for Detection and Diagnosis of Breast Cancer on MRI

Yang Zhang^1,2, 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

Synopsis

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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