mlsanityData to 7 Oct 2026

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

arXiv 2109.03299 ↗First posted 7 Sep 2021eess.IV cs.CVcited by 25

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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.

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

Builds on

25 of 43 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  7. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  8. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  9. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  10. 2015Adversarial Autoencoderscited by 359
  11. 2018The relativistic discriminator: a key element missing from standard GANcited by 234
  12. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 172
13 more
  1. 2019Deep neural network models for computational histopathology: A surveycited by 165
  2. 2020Self supervised contrastive learning for digital histopathologycited by 146
  3. 2020Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotationscited by 89
  4. 2021Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articlescited by 58
  5. 2019Very Long Natural Scenery Image Prediction by Outpaintingcited by 50
  6. 2018Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approachcited by 33
  7. 2019PathologyGAN: Learning deep representations of cancer tissuecited by 32
  8. 2020Self-Supervised Nuclei Segmentation in Histopathological Images Using Attentioncited by 30
  9. 2020Interpretable Survival Prediction for Colorectal Cancer using Deep Learningcited by 30
  10. 2020Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potentialcited by 17
  11. 2017Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Imagescited by 10
  12. 2018Pioneer Networks: Progressively Growing Generative Autoencodercited by 4
  13. 2021Image Synthesis as a Pretext for Unsupervised Histopathological Diagnosiscited by 3

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Citing papers per year: 2022: 8; 2023: 8; 2024: 5; 2025: 4.

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  1. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  2. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 148
  3. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  4. 2022WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinomacited by 32
  5. 2022Towards 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 Analysiscited by 29
  6. 2023AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Imagescited by 18
  7. 2023ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysiscited by 15
  8. 2023Rotation-Agnostic Image Representation Learning for Digital Pathologycited by 15
  9. 2022Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learnerscited by 11
  10. 2022Region-guided CycleGANs for Stain Transfer in Whole Slide Imagescited by 8
  11. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  12. 2023A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Imagescited by 5
13 more
  1. 2025Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challengescited by 5
  2. 2022Precise Location Matching Improves Dense Contrastive Learning in Digital Pathologycited by 4
  3. 2023MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantationcited by 4
  4. 2023Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learningcited by 4
  5. 2024Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learningcited by 4
  6. 2022CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Networkcited by 2
  7. 2023Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Datacited by 2
  8. 2024CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learningcited by 2
  9. 2024Resource Efficient Multi-stain Kidney Glomeruli Segmentation via Self-supervisioncited by 2
  10. 2024Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Reviewcited by 1
  11. 2024Renal Cell Carcinoma subtyping: learning from multi-resolution localizationcited by 0
  12. 2025Hierarchical Self-Supervised Adversarial Training for Robust Vision Models in Histopathologycited by 0
  13. 2025Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learningcited by 0