%0 Journal Article %A HUANG Jun %A LIU Qiyu %A LIU Zhiqin %A WANG Qingfeng %A XIAO He %A XU Weiyun %A ZHOU Ying %T Mass and calcification classification method in mammogram based on multi-view transfer learning %D 2020 %R 10.11772/j.issn.1001-9081.2019101744 %J Journal of Computer Applications %P 1460-1464 %V 40 %N 5 %X

In order to solve the problem of insufficient available training data in the classification task of breast mass and calcification, a multi-view model based on secondary transfer learning was proposed combining with imaging characteristics of mammogram. Firstly, CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) was used to construct the breast local tissue section dataset for the pre-training of the backbone network, and the domain adaptation learning of the backbone network was completed, so the backbone network had the essential ability of capturing pathological features. Then, the backbone network was secondarily transferred to the multi-view model and was fine-tuned based on the dataset of Mianyang Central Hospital. At the same time, the number of positive samples in the training was increased by CBIS-DDSM to improve the generalization ability of the network. The experimental results show that the domain adaption learning and data augmentation strategy improves the performance criteria by 17% averagely and achieves 94% and 90% AUC (Area Under Curve) values for mass and calcification respectively.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019101744