Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging

Motivation: As manual slice-by-slice analysis of breast MR images is both time-consuming and error-prone. Goal(s): To develop a deep learning-based system for the detection and classification of breast lesions in DCE-MRI. Approach: DCE-MRI images were fed into the developed cascade feature pyramid network system(CFPN), feature pyramid network, and faster region-based convolutional neural network for breast lesion detection and classification. Results: CFPN achieved the highest sensitivities in detection at the lowest FPs at both the slice level and the patient level. Impact: DL-based systems can automatically detect and classify breast lesions on DCE-MRI. These results illustrate the potential use of this technique in a clinically relevant setting.