Volume 14 Issue 2
Mar.  2023
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ZENG Li, TANG Hongzhong, WANG Wei, XIE Mingjian, WU Yongjun. Semi-supervised Histopathological Image Segmentation Method Based on Multi-task Learning[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(2): 416-425. doi: 10.12290/xhyxzz.2022-0096
Citation: ZENG Li, TANG Hongzhong, WANG Wei, XIE Mingjian, WU Yongjun. Semi-supervised Histopathological Image Segmentation Method Based on Multi-task Learning[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(2): 416-425. doi: 10.12290/xhyxzz.2022-0096

Semi-supervised Histopathological Image Segmentation Method Based on Multi-task Learning

doi: 10.12290/xhyxzz.2022-0096
Funds:

Joint Fund for Regional Innovation and Development of National Natural Science Foundation in China U19A2083

Natural Science Foundation of Hunan Province in China 2020JJ4588

Natural Science Foundation of Hunan Province in China 2020JJ4090

Open Project of Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University 2020ICIP06

More Information
  • Corresponding author: TANG Hongzhong, E-mail: diandiant@126.com
  • Received Date: 2022-03-02
  • Accepted Date: 2022-06-20
  • Publish Date: 2023-03-30
  • Automatic segmentation of histopathological image is an important step of computer-aided diagnosis, which can reduce the workload of pathologists and improve the efficiency and diagnosis accuracy. This paper introduces a semi-supervised histopathological image segmentation method combined with multi-task learning. This method could simultaneously segment cancer region and classify image using semi-supervised method. Firstly, we used a limited number of pixel-level labels to train a segmentation network, and then the segmentation network and a classification network were trained using some image-level labels. The network parameters were optimized by alternating iterative method in the process of the network training. This method could reduce the annotation cost compared with the standard supervised method for deep learning model. Furthermore, we introduced a dynamically weighted cross entropy loss function to train the network, which could automatically allocate the weight of each pixel by using the probability of classification prediction. This strategy could promote the segmentation network to pay attention to some target regions with the low probability of classification prediction. Therefore, the details of the cancer regions could be preserved. Experimental results on the breast cancer histopathological image verified that our method outperformed other state-of-the-arts on the cancer segmentation performance under the condition of insufficient pixel-level label data.
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