  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.
