GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer
文献类型:期刊论文
作者 | Hu, Weiming5; Li C(李晨)5; Li, Xiaoyan6; Rahaman, Md Mamunur4,5; Ma, Jiquan7; Zhang, Yong6; Chen, Haoyuan5; Liu, Wanli5; Sun CH(孙昌浩)1,5; Yao, Yudong8 |
刊名 | Computers in Biology and Medicine |
出版日期 | 2022 |
卷号 | 142页码:1-9 |
ISSN号 | 0010-4825 |
关键词 | Gastric histopathology Sub-size image Database Image classification |
产权排序 | 4 |
英文摘要 | Background and objective: Gastric cancer is the fifth most common cancer globally, and early detection of gastric cancer is essential to save lives. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, computer-aided diagnostic techniques are challenging to evaluate due to the scarcity of publicly available gastric histopathology image datasets. Methods: In this paper, a noble publicly available Gastric Histopathology Sub-size Image Database (GasHisSDB) is published to identify classifiers’ performance. Specifically, two types of data are included: normal and abnormal, with a total of 245,196 tissue case images. In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation. Seven classical machine learning classifiers, three Convolutional Neural Network classifiers, and a novel transformer-based classifier are selected for testing on image classification tasks. Results: This study performed extensive experiments using traditional machine learning and deep learning methods to prove that the methods of different periods have discrepancies on GasHisSDB. Traditional machine learning achieved the best accuracy rate of 86.08% and a minimum of just 41.12%. The best accuracy of deep learning reached 96.47% and the lowest was 86.21%. Accuracy rates vary significantly across classifiers. Conclusions: To the best of our knowledge, it is the first publicly available gastric cancer histopathology dataset containing a large number of images for weakly supervised learning. We believe that GasHisSDB can attract researchers to explore new algorithms for the automated diagnosis of gastric cancer, which can help physicians and patients in the clinical setting. |
WOS关键词 | CLASSIFICATION |
资助项目 | National Natural Science Foundation of China[61 806 047] ; Fundamental Research Funds for the Central Universities[N2019003] |
WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology |
语种 | 英语 |
WOS记录号 | WOS:000747364900001 |
资助机构 | National Natural Science Foundation of China (No. 61 806 047) ; Fundamental Research Funds for the Central Universities (No. N2019003) |
源URL | [http://ir.sia.cn/handle/173321/30299] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Li C(李晨) |
作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China 2.Department of Radiology, Shengjing Hospital, China Medical University, Shenyang 110122, China 3.Institute of Medical Informatics, University of Luebeck, Luebeck, Germany 4.School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia 5.Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China 6.Department of Pathology, Cancer Hospital, China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, China 7.Department of Computer Science and Technology, Heilongjiang University, Harbin, Heilongjiang 150080, China 8.Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, United States |
推荐引用方式 GB/T 7714 | Hu, Weiming,Li C,Li, Xiaoyan,et al. GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer[J]. Computers in Biology and Medicine,2022,142:1-9. |
APA | Hu, Weiming.,Li C.,Li, Xiaoyan.,Rahaman, Md Mamunur.,Ma, Jiquan.,...&Grzegorzek, Marcin.(2022).GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer.Computers in Biology and Medicine,142,1-9. |
MLA | Hu, Weiming,et al."GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer".Computers in Biology and Medicine 142(2022):1-9. |
入库方式: OAI收割
来源:沈阳自动化研究所
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