Deep learning radiomics in breast cancer with different modalities: Overview and future

摘要

Recent improvements in deep learning radiomics (DLR) extracting high-level features form medical imaging could promote the performance of computer aided diagnosis (CAD) for cancer. Breast cancer is the most frequent cancer among women and prospective achievements have been reported by CAD systems based on deep learning methods for breast imaging. In this paper, we aim to provide a comprehensive overview of the recent research efforts on DLR in breast cancer with different modalities and propose the future directions in this field. First, we respectively summarize and analyze the dataset, architecture, application and evaluation on DLR for breast cancer with three main imaging modalities, i.e., ultrasound, mammography, magnetic resonance imaging. Especially, we provide a survey on deep learning architectures exploited in breast cancer, including discriminative architectures and generative architectures. Then, we propose some potential challenges along with future research directions as references to the clinical treatment management and decision making utilizing such breast cancer CAD systems.