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

TL;DR

This paper introduces a novel unsupervised GAN-based approach for learning cell-level visual representations in histopathology images, enabling label-free classification and analysis of cellular components.

Contribution

It proposes a new GAN architecture with a specialized loss function for robust, label-free cell-level feature learning and interpretable visualization.

Findings

Achieved promising unsupervised classification of bone marrow cells.

Developed a pipeline for histopathology image classification using learned features.

Model is easy to train and does not require labels.

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.