# Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology

arXiv: https://arxiv.org/abs/2109.03299 · HTML: https://mlsanity.com/paper/2109.03299/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 43 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations](https://mlsanity.com/paper/2008.05571/) (2020)
- [Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles](https://mlsanity.com/paper/2103.05121/) (2021)
- [Very Long Natural Scenery Image Prediction by Outpainting](https://mlsanity.com/paper/1912.12688/) (2019)
- [Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach](https://mlsanity.com/paper/1802.02212/) (2018)
- [PathologyGAN: Learning deep representations of cancer tissue](https://mlsanity.com/paper/1907.02644/) (2019)
- [Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention](https://mlsanity.com/paper/2007.08373/) (2020)
- [Interpretable Survival Prediction for Colorectal Cancer using Deep Learning](https://mlsanity.com/paper/2011.08965/) (2020)
- [Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential](https://mlsanity.com/paper/2004.14936/) (2020)
- [Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images](https://mlsanity.com/paper/1704.00406/) (2017)
- [Pioneer Networks: Progressively Growing Generative Autoencoder](https://mlsanity.com/paper/1807.03026/) (2018)
- [Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosis](https://mlsanity.com/paper/2104.13797/) (2021)

## Built on by (most cited first, 25 of 25)

- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Benchmarking Self-Supervised Learning on Diverse Pathology Datasets](https://mlsanity.com/paper/2212.04690/) (2022)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)
- [WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma](https://mlsanity.com/paper/2204.06455/) (2022)
- [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)
- [AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images](https://mlsanity.com/paper/2303.00865/) (2023)
- [ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis](https://mlsanity.com/paper/2304.01053/) (2023)
- [Rotation-Agnostic Image Representation Learning for Digital Pathology](https://mlsanity.com/paper/2311.08359/) (2023)
- [Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners](https://mlsanity.com/paper/2205.09048/) (2022)
- [Region-guided CycleGANs for Stain Transfer in Whole Slide Images](https://mlsanity.com/paper/2208.12847/) (2022)
- [Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study](https://mlsanity.com/paper/2505.01109/) (2025)
- [A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images](https://mlsanity.com/paper/2303.11019/) (2023)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology](https://mlsanity.com/paper/2212.12105/) (2022)
- [MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation](https://mlsanity.com/paper/2303.12445/) (2023)
- [Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning](https://mlsanity.com/paper/2309.06439/) (2023)
- [Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning](https://mlsanity.com/paper/2403.14616/) (2024)
- [CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network](https://mlsanity.com/paper/2203.15078/) (2022)
- [Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data](https://mlsanity.com/paper/2303.10342/) (2023)
- [CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning](https://mlsanity.com/paper/2408.04262/) (2024)
- [Resource Efficient Multi-stain Kidney Glomeruli Segmentation via Self-supervision](https://mlsanity.com/paper/2412.15389/) (2024)
- [Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review](https://mlsanity.com/paper/2410.19820/) (2024)
- [Renal Cell Carcinoma subtyping: learning from multi-resolution localization](https://mlsanity.com/paper/2411.09471/) (2024)
- [Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathology](https://mlsanity.com/paper/2503.10629/) (2025)
- [Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning](https://mlsanity.com/paper/2511.06958/) (2025)

## Abstract (arXiv)

The examination of histopathology images is considered to be the gold standard for the diagnosis and stratification of cancer patients. A key challenge in the analysis of such images is their size, which can run into the gigapixels and can require tedious screening by clinicians. With the recent advances in computational medicine, automatic tools have been proposed to assist clinicians in their everyday practice. Such tools typically process these large images by slicing them into tiles that can then be encoded and utilized for different clinical models. In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field. In particular, we developed a progressively grown generative model with the objective of visual field expansion. Thus trained, our model learns to generate different tissue types with fine details, while simultaneously learning powerful representations that can be used for different clinical endpoints, all in a self-supervised way. To evaluate the performance of our model, we conducted classification experiments on CAMELYON17 and CRC benchmark datasets, comparing favorably to other self-supervised and pre-trained strategies that are commonly used in digital pathology. Our code is available at https://github.com/jcboyd/cdpath21-gan.