题名 | Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation |
作者 | |
发表日期 | 2023 |
发表期刊 | ELECTRONIC RESEARCH ARCHIVE 影响因子和分区 |
语种 | 英语 |
原始文献类型 | Article |
关键词 | active learning pathology images classification latent representation samples selection ALHS strategy |
摘要 | Most countries worldwide continue to encounter a pathologist shortage, significantly impeding the timely diagnosis and effective treatment of cancer patients. Deep learning techniques have performed remarkably well in pathology image analysis; however, they require expert pathologists to annotate substantial pathology image data. This study aims to minimize the need for data annotation to analyze pathology images. Active learning (AL) is an iterative approach to search for a few high-quality samples to train a model. We propose our active learning framework, which first learns latent representations of all pathology images by an auto-encoder to train a binary classification model, and then selects samples through a novel ALHS (Active Learning Hybrid Sampling) strategy. This strategy can effectively alleviate the sample redundancy problem and allows for more informative and diverse examples to be selected. We validate the effectiveness of our method by undertaking classification tasks on two cancer pathology image datasets. We achieve the target performance of 90% accuracy using 25% labeled samples in Kather's dataset and reach 88% accuracy using 65% labeled data in BreakHis dataset, which means our method can save 75% and 35% of the annotation budget in the two datasets, respectively. |
资助项目 | Medico -Engineering Cooperation Funds from University of Electronic Science and Technology of China [ZYGX2021YGLH213, ZYGX2022YGRH016]; Zhejiang Provincial Natural Science Foundation of China [LGF22G010009]; Municipal Government of Quzhou [2022D018, 2022D029] |
出版者 | AMER INST MATHEMATICAL SCIENCES-AIMS |
ISSN | 2688-1594 |
EISSN | 2688-1594 |
卷号 | 31期号:9页码:5340-5361 |
DOI | 10.3934/era.2023271 |
页数 | 22 |
WOS类目 | Mathematics |
WOS研究方向 | Mathematics |
WOS记录号 | WOS:001041201200001 |
收录类别 | SCIE ; SCOPUS |
URL | 查看原文 |
SCOPUSEID | 2-s2.0-85168829648 |
通讯作者地址 | [Wu, Lei]School of Mathematical Sciences,University of Electronic Science and Technology of China,Chengdu,611731,China |
Scopus学科分类 | Mathematics (all) |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.wmu.edu.cn/handle/3ETUA0LF/182306 |
专题 | 其他_温州医科大学附属衢州医院 |
通讯作者 | Wu, Lei |
作者单位 | 1.School of Mathematical Sciences,University of Electronic Science and Technology of China,Chengdu,611731,China; 2.Yangtze Delta Region Institute (Quzhou),University of Electronic Science and Technology of China,Quzhou,314099,China; 3.School of Computer and Software Engineering,Xihua University,Chengdu,611731,China; 4.The Quzhou Affiliated Hospital of Wenzhou Medical University,Quzhou People’s Hospital,Quzhou,324000,China |
推荐引用方式 GB/T 7714 | Sun, Yixin,Wu, Lei,Chen, Peng,et al. Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation[J]. ELECTRONIC RESEARCH ARCHIVE,2023,31(9):5340-5361. |
APA | Sun, Yixin, Wu, Lei, Chen, Peng, Zhang, Feng, & Xu, Lifeng. (2023). Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation. ELECTRONIC RESEARCH ARCHIVE, 31(9), 5340-5361. |
MLA | Sun, Yixin,et al."Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation".ELECTRONIC RESEARCH ARCHIVE 31.9(2023):5340-5361. |
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