In Medical physics ; h5-index 59.0
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 Multi-context Multi-task Learning (MCMTL).
METHODS : In the first stage, SRL focuses on finding suspicious regions (regions of interest, ROIs) and extracting multi-size patches of these suspicious regions. A set of bounding boxes with different size is used to extract multi-size patches, which aim to capture diverse context information. In the second stage, MCMTL networks integrate features from multi-size 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.
Shen Rongbo, Zhou Ke, Yan Kezhou, Tian Kuan, Zhang Jun