IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
文献类型:期刊论文
作者 | Chen, Haoyuan2; Li C(李晨)2; Li, Xiaoyan3; Rahaman, Md Mamunur2; Hu, Weiming2; Li, Yixin2; Liu, Wanli2; Sun CH(孙昌浩)2,4; Sun HZ(孙洪赞)5; Huang, Xinyu1 |
刊名 | Computers in Biology and Medicine |
出版日期 | 2022 |
卷号 | 143页码:1-17 |
ISSN号 | 0010-4825 |
关键词 | Colorectal cancer histopathology image Attention mechanism Interactivity learning Image classificatio |
产权排序 | 3 |
英文摘要 | In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interaction. In this paper, we propose an IL-MCAM framework. It is based on attention mechanisms and interactive learning. The proposed IL-MCAM framework includes two stages: automatic learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel attention mechanism model containing three different attention mechanism channels and convolutional neural networks is used to extract multi-channel features for classification. In the IL stage, the proposed IL-MCAM framework continuously adds misclassified images to the training set in an interactive approach, which improves the classification ability of the MCAM model. We carried out a comparison experiment on our dataset and an extended experiment on the HE-NCT-CRC-100K dataset to verify the performance of the proposed IL-MCAM framework, achieving classification accuracies of 98.98% and 99.77%, respectively. In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels. The experimental results show that the proposed IL-MCAM framework has excellent performance in the colorectal histopathological image classification tasks. |
语种 | 英语 |
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/173321/30335] |
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); Li, Xiaoyan |
作者单位 | 1.Institute of Medical Informatics, University of Luebeck, Germany 2.Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China 3.Department of Pathology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, China 4.Shenyang Institute of Automation, Chinese Academy of Sciences, China 5.Department of Radiology, Shengjing Hospital of China Medical University, China |
推荐引用方式 GB/T 7714 | Chen, Haoyuan,Li C,Li, Xiaoyan,et al. IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach[J]. Computers in Biology and Medicine,2022,143:1-17. |
APA | Chen, Haoyuan.,Li C.,Li, Xiaoyan.,Rahaman, Md Mamunur.,Hu, Weiming.,...&Grzegorzek, Marcin.(2022).IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach.Computers in Biology and Medicine,143,1-17. |
MLA | Chen, Haoyuan,et al."IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach".Computers in Biology and Medicine 143(2022):1-17. |
入库方式: OAI收割
来源:沈阳自动化研究所
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