# Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks Source: [Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:06:03.000Z Authors: Bo Hu, Ye Tang, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu Published: 2017-11-30T10:53:56.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()