Multi-Context Multi-Task Learning Networks for Mass Detection in Mammogram.

Purpose: In this paper, for the purpose of accurate and efficient mass detection, we propose a new deep learning framework, including two major stages: Suspicious region localization (SRL) and Multicontext Multitask Learning (MCMTL). Methods: In the first stage, SRL focuses on finding suspicious regions [regions of interest (ROIs)] and extracting multisize patches of these suspicious regions. A set of bounding boxes with different size is used to extract multisize patches, which aim to capture diverse context information. In the second stage, MCMTL networks integrate features from multisize patches of suspicious regions for classification and segmentation simultaneously, where the purpose of this stage is to keep the true positive suspicious regions and to reduce the false positive suspicious regions. Results: According to the experimental results on two public datasets (i.e., CBIS-DDSM and INBreast), our method achieves the overall performance of 0.812 TPR@2.53 FPI and 0.919 TPR@0.12 FPI on test sets, respectively. Conclusions: Our proposed method suggests comparable performance to the state-of-the-art methods. (C) 2019 American Association of Physicists in Medicine