文献类型：期刊文献

英文题名：Two and multiple categorization of breast pathological images by transfer learning

作者：Yan, Jiaxin[1]; Wang, Bei[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, [East China University of Science and Technology], Ministry of Education, Shanghai, 200237, China

年份：2021

起止页码：84

外文期刊名：ICIIBMS 2021 - 6th International Conference on Intelligent Informatics and Biomedical Sciences

收录：EI(收录号：20220711621547)

语种：英文

外文关键词：Computer aided instruction - Diseases - Medical imaging - Pathology - Deep learning - Learning systems

摘要：Breast cancer is the most common cancer in women worldwide. By using artificial intelligence technology to assist doctors in pathological diagnosis can effectively improve the efficiency of cancer diagnosis. However, the computer-aided diagnosis (CAD) has the problems of long training time for large-resolution pathological pictures and insufficient data that can be marked for training. In this paper, a transfer learning model is developed for the pathological diagnosis of breast cancer to overcome those problems. Four common deep learning models (VGGnet, Resnet, Densenet, Mobilenet) were adopted to train breast pathology images under four different resolutions (40X, 100X, 200X, 400X). A transfer learning framework was established to distinguish benign and malignant breast pathology and their subtypes. The accuracy of the two-classification model can reach 91.24% at the best magnification (200X), and the average accuracy is 89.31%. At the same time, the multi-classification model for the eight subtypes of pathological sections also achieved quite satisfied results. It is indicated that the presented transfer learning framework has great potential for exploring the CAD of breast cancer. ? 2021 IEEE.