# PathologyGAN: Learning deep representations of cancer tissue

arXiv: https://arxiv.org/abs/1907.02644 · HTML: https://mlsanity.com/paper/1907.02644/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 30 of 32)

- [Self-supervised driven consistency training for annotation efficient histopathology image analysis](https://mlsanity.com/paper/2102.03897/) (2021)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology](https://mlsanity.com/paper/2306.13384/) (2023)
- [Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology](https://mlsanity.com/paper/2109.03299/) (2021)
- [Synthetic Augmentation with Large-scale Unconditional Pre-training](https://mlsanity.com/paper/2308.04020/) (2023)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2401.13699/) (2024)
- [PathDiff: Histopathology Image Synthesis with Unpaired Text and Mask Conditions](https://mlsanity.com/paper/2506.23440/) (2025)
- [Adversarial learning of cancer tissue representations](https://mlsanity.com/paper/2108.02223/) (2021)
- [Recommendations on test datasets for evaluating AI solutions in pathology](https://mlsanity.com/paper/2204.14226/) (2022)
- [Region-guided CycleGANs for Stain Transfer in Whole Slide Images](https://mlsanity.com/paper/2208.12847/) (2022)
- [Disentangling representations of retinal images with generative models](https://mlsanity.com/paper/2402.19186/) (2024)
- [Deep Learning Generates Synthetic Cancer Histology for Explainability and Education](https://mlsanity.com/paper/2211.06522/) (2022)
- [SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts](https://mlsanity.com/paper/2212.13780/) (2022)
- [Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology](https://mlsanity.com/paper/2302.06549/) (2023)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [Self adversarial attack as an augmentation method for immunohistochemical stainings](https://mlsanity.com/paper/2103.11362/) (2021)
- [Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology](https://mlsanity.com/paper/2509.17847/) (2025)
- [GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation](https://mlsanity.com/paper/2309.01487/) (2023)
- [DEPAS: De-novo Pathology Semantic Masks using a Generative Model](https://mlsanity.com/paper/2302.06513/) (2023)
- [Deepfake histological images for enhancing digital pathology](https://mlsanity.com/paper/2206.08308/) (2022)
- [Towards Highly Expressive Machine Learning Models of Non-Melanoma Skin Cancer](https://mlsanity.com/paper/2207.05749/) (2022)
- [Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout](https://mlsanity.com/paper/2305.05006/) (2023)
- [Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization](https://mlsanity.com/paper/2410.05114/) (2024)
- [Comparative Analysis of Diffusion Generative Models in Computational Pathology](https://mlsanity.com/paper/2411.15719/) (2024)
- [PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation](https://mlsanity.com/paper/2411.16515/) (2024)
- [Generative Modeling of Complex-Valued Brain MRI Data](https://mlsanity.com/paper/2604.14800/) (2026)
- [Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection](https://mlsanity.com/paper/2205.10691/) (2022)
- [SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data](https://mlsanity.com/paper/2207.14650/) (2022)
- [Representation Learning for Non-Melanoma Skin Cancer using a Latent Autoencoder](https://mlsanity.com/paper/2209.01779/) (2022)

## Abstract (arXiv)

Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the cost of high quality labels from patients data. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alternative path to further understand cancer tissue phenotypes, capturing tissue morphologies. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on two different datasets, an H&E colorectal cancer tissue from the National Center for Tumor diseases (NCT) and an H&E breast cancer tissue from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH). Composed of 86 slide images and 576 TMAs respectively. We show that our model generates high quality images, with a FID of 16.65 (breast cancer) and 32.05 (colorectal cancer). We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, images, and pretrained models are available at https://github.com/AdalbertoCq/Pathology-GAN