1 research outputs found
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
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.Comment: Accepted for publication in IEEE Journal of Biomedical and Health
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