Knowledge Commons of Institute of Automation,CAS
Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis | |
Shen, Tianyu1,4,5; Wang, Jiangong2,4,5; Gou, Chao6; Wang, Fei-Yue3,4,7 | |
发表期刊 | IEEE TRANSACTIONS ON FUZZY SYSTEMS |
ISSN | 1063-6706 |
2020 | |
卷号 | 28期号:12页码:3204-3218 |
摘要 | Breast cancer diagnosis based on medical imaging necessitates both fine-grained lesion segmentation and disease grading. Although deep learning (DL) offers an emerging and powerful paradigm of feature learning for these two tasks, it is hampered from popularizing in practical application due to the lack of interpretability, generalization ability, and large labeled training sets. In this article, we propose a hierarchical fused model based on DL and fuzzy learning to overcome the drawbacks for pixelwise segmentation and disease grading of mammography breast images. The proposed system consists of a segmentation model (ResU-segNet) and a hierarchical fuzzy classifier (HFC) that is a fusion of interval type-2 possibilistic fuzzy c-means and fuzzy neural network. The ResU-segNet segments the masks of mass regions from the images through convolutional neural networks, while the HFC encodes the features from mass images and masks to obtain the disease grading through fuzzy representation and rule-based learning. Through the integration of feature extraction aided by domain knowledge and fuzzy learning, the system achieves favorable performance in a few-shot learning manner, and the deterioration of cross-dataset generalization ability is alleviated. In addition, the interpretability is further enhanced. The effectiveness of the proposed system is analyzed on the publicly available mammogram database of INbreast and a private database through cross-validation. Thorough comparative experiments are also conducted and demonstrated. |
关键词 | Image segmentation Biomedical imaging Fuzzy sets Breast cancer Breast cancer deep learning (DL) fuzzy classifier (FC) interval type-2 possibilistic fuzzy c-means (IT2PFCM) |
DOI | 10.1109/TFUZZ.2020.3013681 |
关键词[WOS] | NEURAL-NETWORK ; CLASSIFICATION ; MAMMOGRAMS ; ALGORITHM ; TUMOR |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[61533019] ; Key Research and Development Program of Guangzhou[202007050002] |
项目资助者 | National Natural Science Foundation of China ; Key Research and Development Program of Guangzhou |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000595527100014 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42708 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Gou, Chao |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Social Comp, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Pattern Recognit & Intelligent Syst, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 6.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China 7.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shen, Tianyu,Wang, Jiangong,Gou, Chao,et al. Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2020,28(12):3204-3218. |
APA | Shen, Tianyu,Wang, Jiangong,Gou, Chao,&Wang, Fei-Yue.(2020).Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis.IEEE TRANSACTIONS ON FUZZY SYSTEMS,28(12),3204-3218. |
MLA | Shen, Tianyu,et al."Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis".IEEE TRANSACTIONS ON FUZZY SYSTEMS 28.12(2020):3204-3218. |
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