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作者

Luhao Sun, Bowen Han, Wenzong Jiang, Weifeng Liu, Baodi Liu, Dapeng Tao, Zhiyong Yu, Chao Li

作者单位

Yunnan United Vision Technology Co., Ltd., Yunnan 650504, China. ⁶ Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China. Electronic address: zyyu@sdfmu.edu.cn. ⁷ Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China. Electronic address: lichao19890305@12 Affiliations ¹ Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China. ² School of Computer Science and Technology, Tongji University, Shanghai 201804, China. ³ The College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China. ⁴ The College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China. ⁵ The School of Information Science and Engineering, Yunnan University, Yunnan 650504, China com.

刊名

Medical image analysis

年份

2024

卷号

Vol.100

页码

103399

ISSN

1361-8423

关键词

Breast cancer Early diagnosis Full-field mammogram Region selection.

摘要

Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early diagnosis of breast cancer, the classification of mammogram images is an important work of computer-aided diagnosis systems. With the development of deep learning in CAD, deep convolutional neural networks have been shown to have the ability to complete the classification of breast cancer tumor patches with high quality, which makes most previous...更多

Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early diagnosis of breast cancer, the classification of mammogram images is an important work of computer-aided diagnosis systems. With the development of deep learning in CAD, deep convolutional neural networks have been shown to have the ability to complete the classification of breast cancer tumor patches with high quality, which makes most previous CNN-based full-field mammography classification methods rely on region of interest or segmentation annotation to enable the model to locate and focus on small tumor regions. However, the dependence on ROI greatly limits the development of CAD, because obtaining a large number of reliable ROI annotations is expensive and difficult. Some full-field mammography image classification algorithms use multi-stage training or multi-feature extractors to get rid of the dependence on ROI, which increases the computational amount of the model and feature redundancy. In order to reduce the cost of model training and make full use of the feature extraction capability of CNN, we propose a deep multi-scale region selection network in deep features for end-to-end training to classify full-field mammography without ROI or segmentation annotation. Inspired by the idea of multi-example learning and the patch classifier, MRSN filters the feature information and saves only the feature information of the tumor region to make the performance of the full-field image classifier closer to the patch classifier. MRSN first scores different regions under different dimensions to obtain the location information of tumor regions. Then, a few high-scoring regions are selected by location information as feature representations of the entire image, allowing the model to focus on the tumor region. Experiments on two public datasets and one private dataset prove that the proposed MRSN achieves the most advanced performance.收起

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