A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification
文献类型:期刊论文
作者 | Li, Yixin1; Wu, Xinran1; Li C(李晨)1; Li, Xiaoyan3; Chen, Haoyuan1; Sun CH(孙昌浩)1,5; Rahaman, Md Mamunur1; Yao, Yudong4; Zhang, Yong3; Jiang, Tao2 |
刊名 | APPLIED INTELLIGENCE |
出版日期 | 2022 |
页码 | 1-22 |
ISSN号 | 0924-669X |
关键词 | Attention mechanism Histopathology image Conditional random field Gastric cancer Image classification |
产权排序 | 3 |
英文摘要 | In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected, and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from the patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field. In addition, the AM module and transfer learning technique allow the network to generalize well to other types of image data except histopathology images, and we obtain 95.5% and 95.8% accuracies on IG02 and Oxford-IIIT Pet Datasets. |
WOS关键词 | DEEP ; SEGMENTATION ; CANCER |
资助项目 | National Natural Science Foundation of China[61806047] |
WOS研究方向 | Computer Science |
语种 | 英语 |
WOS记录号 | WOS:000740170900003 |
资助机构 | National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61806047] |
源URL | [http://ir.sia.cn/handle/173321/30256] |
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.Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China 2.Control Engineering College, Chengdu University of Information Technology, Chengdu, People’s Republic of China 3.Liaoning Cancer Hospital and Institute, China Medical University, Shenyang, China 4.Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
推荐引用方式 GB/T 7714 | Li, Yixin,Wu, Xinran,Li C,et al. A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification[J]. APPLIED INTELLIGENCE,2022:1-22. |
APA | Li, Yixin.,Wu, Xinran.,Li C.,Li, Xiaoyan.,Chen, Haoyuan.,...&Jiang, Tao.(2022).A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification.APPLIED INTELLIGENCE,1-22. |
MLA | Li, Yixin,et al."A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification".APPLIED INTELLIGENCE (2022):1-22. |
入库方式: OAI收割
来源:沈阳自动化研究所
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