Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks

Abstract

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.

arXiv commentAccepted for publication in IEEE Journal of Biomedical and Health Informatics

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• Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology
• HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis
• A multi-stage GAN for multi-organ chest X-ray image generation and segmentation
• Adversarial learning of cancer tissue representations
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