期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 23, 期 3, 页码 1316-1328出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2852639
关键词
Unsupervised learning; representation learning; generative adversarial networks; classification; cell
类别
资金
- Technology and Innovation Commission of Shenzhen in China [shenfagai 2016-627]
- Microsoft Research under the eHealth program
- National Natural Science Foundation in China [81771910]
- National Science and Technology Major Project of the Ministry of Science and Technology in China [2017YFC0110903]
- Beijing Natural Science Foundation in China [4152033]
- Beijing Young Talent Project in China
- Fundamental Research Funds for the Central Universities of China under State Key Laboratory of Software Development Environment in Beihang University in China [SKLSDE-2017ZX-08]
- 111 Project in China [B13003]
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation, we can obtain a rich mix of features that are highly reusable for various tasks, such as cell-level classification, nuclei segmentation, and cell counting. In this paper, we propose a unified generative adversarial networks architecture with a new formulation of loss to perform robust cell-level visual representation learning in an unsupervised setting. Our model is not only label-free and easily trained but also capable of cell-level unsupervised classification with interpretable visualization, which achieves promising results in the unsupervised classification of bone marrow cellular components. Based on the proposed cell-level visual representation learning, we further develop a pipeline that exploits the varieties of cellular elements to perform histopathology image classification, the advantages of which are demonstrated on bone marrow datasets.
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