# Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation

arXiv: https://arxiv.org/abs/2102.01678 · HTML: https://mlsanity.com/paper/2102.01678/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks](https://mlsanity.com/paper/1905.13545/) (2019)
- [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [Domain Agnostic Learning with Disentangled Representations](https://mlsanity.com/paper/1904.12347/) (2019)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Style Augmentation: Data Augmentation via Style Randomization](https://mlsanity.com/paper/1809.05375/) (2018)
- [Style transfer-based image synthesis as an efficient regularization technique in deep learning](https://mlsanity.com/paper/1905.10974/) (2019)
- [StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification](https://mlsanity.com/paper/2007.05008/) (2020)

## Built on by (most cited first, 26 of 26)

- [Domain Generalization for Medical Image Analysis: A Review](https://mlsanity.com/paper/2310.08598/) (2023)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation](https://mlsanity.com/paper/2206.01737/) (2022)
- [Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology](https://mlsanity.com/paper/2206.14973/) (2022)
- [Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction](https://mlsanity.com/paper/2411.14001/) (2024)
- [IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification](https://mlsanity.com/paper/2208.11052/) (2022)
- [Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer](https://mlsanity.com/paper/2301.02933/) (2023)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology](https://mlsanity.com/paper/2306.04527/) (2023)
- [Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation](https://mlsanity.com/paper/2401.09496/) (2024)
- [Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models](https://mlsanity.com/paper/2208.11563/) (2022)
- [Nuclear Segmentation and Classification: On Color & Compression Generalization](https://mlsanity.com/paper/2301.03418/) (2023)
- [Test Time Transform Prediction for Open Set Histopathological Image Recognition](https://mlsanity.com/paper/2206.10033/) (2022)
- [HistoStarGAN: A Unified Approach to Stain Normalisation, Stain Transfer and Stain Invariant Segmentation in Renal Histopathology](https://mlsanity.com/paper/2210.09798/) (2022)
- [PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation](https://mlsanity.com/paper/2505.22522/) (2025)
- [A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning](https://mlsanity.com/paper/2111.05882/) (2021)
- [Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review](https://mlsanity.com/paper/2211.14847/) (2022)
- [Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-rays](https://mlsanity.com/paper/2302.13991/) (2023)
- [How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images](https://mlsanity.com/paper/2206.07599/) (2022)
- [Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization](https://mlsanity.com/paper/2310.20638/) (2023)
- [Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review](https://mlsanity.com/paper/2410.19820/) (2024)
- [FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology](https://mlsanity.com/paper/2605.14590/) (2026)
- [PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology Images](https://mlsanity.com/paper/2406.15685/) (2024)
- [Ensemble of Pathology Foundation Models for MIDOG 2025 Track 2: Atypical Mitosis Classification](https://mlsanity.com/paper/2509.02591/) (2025)
- [CLEAR: Causal Learning Framework For Robust Histopathology Tumor Detection Under Out-Of-Distribution Shifts](https://mlsanity.com/paper/2510.14273/) (2025)
- [Multimodal LLMs Outperform Pathology Foundation Models in Cross-Domain Histological Similarity](https://mlsanity.com/paper/2609.32876/) (2026)

## Abstract (arXiv)

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from non-medical style source such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology.