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

arXiv: https://arxiv.org/abs/1711.11317 · HTML: https://mlsanity.com/paper/1711.11317/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2017.

## Built on by (most cited first, 17 of 17)

- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2103.08454/) (2021)
- [A multi-stage GAN for multi-organ chest X-ray image generation and segmentation](https://mlsanity.com/paper/2106.05132/) (2021)
- [Adversarial learning of cancer tissue representations](https://mlsanity.com/paper/2108.02223/) (2021)
- [Semi-Supervised Disease Classification based on Limited Medical Image Data](https://mlsanity.com/paper/2405.04295/) (2024)
- [VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology](https://mlsanity.com/paper/2303.04696/) (2023)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [Unsupervised Representation Learning from Pathology Images with Multi-directional Contrastive Predictive Coding](https://mlsanity.com/paper/2105.05345/) (2021)
- [Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images](https://mlsanity.com/paper/2205.15530/) (2022)
- [Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks](https://mlsanity.com/paper/2308.14066/) (2023)
- [CCRL: Contrastive Cell Representation Learning](https://mlsanity.com/paper/2208.06445/) (2022)
- [HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis](https://mlsanity.com/paper/2405.04902/) (2024)
- [A novel adversarial learning strategy for medical image classification](https://mlsanity.com/paper/2206.11501/) (2022)
- [Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies](https://mlsanity.com/paper/2203.04107/) (2022)
- [Generative Modeling Helps Weak Supervision (and Vice Versa)](https://mlsanity.com/paper/2203.12023/) (2022)
- [Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides](https://mlsanity.com/paper/2304.09314/) (2023)
- [Object Tracking in a 360o View: A Novel Perspective on Bridging the Gap to Biomedical Advancements](https://mlsanity.com/paper/2412.01119/) (2024)

## Abstract (arXiv)

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.