1. Classification of microcalcification clusters in digital breast tomosynthesis using ensemble convolutional neural network
- Author
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Xiaodong Yang, Jian Zheng, You Meng, Zhuangzhi Yan, Yunsong Peng, Bingbing Xiao, Shuangqing Chen, and Haotian Sun
- Subjects
Computer science ,Biomedical Engineering ,CAD ,Breast Neoplasms ,02 engineering and technology ,Convolutional neural network ,Digital breast tomosynthesis ,030218 nuclear medicine & medical imaging ,Convolution ,Biomaterials ,03 medical and health sciences ,0302 clinical medicine ,Ensemble learning ,0202 electrical engineering, electronic engineering, information engineering ,False positive paradox ,Medical technology ,Humans ,Radiology, Nuclear Medicine and imaging ,Diagnosis, Computer-Assisted ,R855-855.5 ,Convolution neural network ,Radiological and Ultrasound Technology ,Receiver operating characteristic ,business.industry ,Deep learning ,Research ,Calcinosis ,Pattern recognition ,General Medicine ,Microcalcification cluster ,Classification ,ROC Curve ,020201 artificial intelligence & image processing ,Female ,Artificial intelligence ,Neural Networks, Computer ,business ,F1 score ,Mammography - Abstract
Background The classification of benign and malignant microcalcification clusters (MCs) is an important task for computer-aided diagnosis (CAD) of digital breast tomosynthesis (DBT) images. Influenced by imaging method, DBT has the characteristic of anisotropic resolution, in which the resolution of intra-slice and inter-slice is quite different. In addition, the sharpness of MCs in different slices of DBT is quite different, among which the clearest slice is called focus slice. These characteristics limit the performance of CAD algorithms based on standard 3D convolution neural network (CNN). Methods To make full use of the characteristics of the DBT, we proposed a new ensemble CNN, which consists of the 2D ResNet34 and the anisotropic 3D ResNet to extract the 2D focus slice features and 3D contextual features of MCs, respectively. Moreover, the anisotropic 3D convolution is used to build 3D ResNet to avoid the influence of DBT anisotropy. Results The proposed method was evaluated on 495 MCs in DBT images of 275 patients, which are collected from our collaborative hospital. The area under the curve (AUC) of receiver operating characteristic (ROC) and accuracy of classifying benign and malignant MCs using decision-level ensemble strategy were 0.8837 and 82.00%, which were significantly higher than the experimental results of 2D ResNet34 (AUC: 0.8264, ACC: 76.00%) and anisotropic 3D ResNet (AUC: 0.8455, ACC: 76.00%). Compared with the results of 3D features classification in the radiomics, the AUC of the deep learning method with decision-level ensemble strategy was improved by 0.0435, and the F1 score was improved from 79.37 to 85.71%. More importantly, the sensitivity increased from 78.13 to 84.38%, and the specificity increased from 66.67 to 77.78%, which effectively reduced the false positives of diagnosis Conclusion The results fully prove that the ensemble CNN can effectively integrate 2D features and 3D features, improve the classification performance of benign and malignant MCs in DBT, and reduce the false positives.
- Published
- 2021