# Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification

arXiv: https://arxiv.org/abs/2111.06399 · HTML: https://mlsanity.com/paper/2111.06399/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 52 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1710.10916/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [Modulating early visual processing by language](https://mlsanity.com/paper/1707.00683/) (2017)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules](https://mlsanity.com/paper/1905.05393/) (2019)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Transferring GANs: generating images from limited data](https://mlsanity.com/paper/1805.01677/) (2018)
- [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks](https://mlsanity.com/paper/1810.10863/) (2018)
- [Image Generation From Small Datasets via Batch Statistics Adaptation](https://mlsanity.com/paper/1904.01774/) (2019)
- [Learning to Compose Domain-Specific Transformations for Data Augmentation](https://mlsanity.com/paper/1709.01643/) (2017)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Perceptual Generative Adversarial Networks for Small Object Detection](https://mlsanity.com/paper/1706.05274/) (2017)
- [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network](https://mlsanity.com/paper/1806.05473/) (2018)
- [High-Fidelity Image Generation With Fewer Labels](https://mlsanity.com/paper/1903.02271/) (2019)

## Built on by (most cited first, 30 of 35)

- [A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images](https://mlsanity.com/paper/2209.13167/) (2022)
- [DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology](https://mlsanity.com/paper/2306.13384/) (2023)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Augmenting medical image classifiers with synthetic data from latent diffusion models](https://mlsanity.com/paper/2308.12453/) (2023)
- [NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models](https://mlsanity.com/paper/2303.11477/) (2023)
- [Synthetic Augmentation with Large-scale Unconditional Pre-training](https://mlsanity.com/paper/2308.04020/) (2023)
- [ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis](https://mlsanity.com/paper/2304.01053/) (2023)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Analyzing Tumors by Synthesis](https://mlsanity.com/paper/2409.06035/) (2024)
- [Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale](https://mlsanity.com/paper/2311.08199/) (2023)
- [Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification](https://mlsanity.com/paper/2203.07815/) (2022)
- [Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment](https://mlsanity.com/paper/2210.08054/) (2022)
- [SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis](https://mlsanity.com/paper/2209.02412/) (2022)
- [VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Annotation-Free Pathological Image Classification](https://mlsanity.com/paper/2403.15836/) (2024)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification](https://mlsanity.com/paper/2307.06182/) (2023)
- [HistoStarGAN: A Unified Approach to Stain Normalisation, Stain Transfer and Stain Invariant Segmentation in Renal Histopathology](https://mlsanity.com/paper/2210.09798/) (2022)
- [CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification](https://mlsanity.com/paper/2301.11513/) (2023)
- [FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification](https://mlsanity.com/paper/2302.12662/) (2023)
- [AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image](https://mlsanity.com/paper/2303.06371/) (2023)
- [Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening](https://mlsanity.com/paper/2402.14707/) (2024)
- [Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses](https://mlsanity.com/paper/2407.16634/) (2024)
- [Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data](https://mlsanity.com/paper/2504.12351/) (2025)
- [Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images](https://mlsanity.com/paper/2309.12245/) (2023)
- [Unleashing the Potential of Synthetic Images: A Study on Histopathology Image Classification](https://mlsanity.com/paper/2409.16002/) (2024)
- [Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review](https://mlsanity.com/paper/2410.19820/) (2024)
- [SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis](https://mlsanity.com/paper/2503.13799/) (2025)
- [Preparing data for pathological artificial intelligence with clinical-grade performance](https://mlsanity.com/paper/2205.10748/) (2022)
- [Automated segmentation and morphological characterization of placental histology images based on a single labeled image](https://mlsanity.com/paper/2210.03566/) (2022)
- [Data Efficient Contrastive Learning in Histopathology using Active Sampling](https://mlsanity.com/paper/2303.16247/) (2023)

## Abstract (arXiv)

Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large number of expert annotations that can be expensive and time-consuming to collect. Meanwhile, accurate classification of image patches cropped from whole-slide images is essential for standard sliding window based histopathology slide classification methods. To mitigate these issues, we propose a carefully designed conditional GAN model, namely HistoGAN, for synthesizing realistic histopathology image patches conditioned on class labels. We also investigate a novel synthetic augmentation framework that selectively adds new synthetic image patches generated by our proposed HistoGAN, rather than expanding directly the training set with synthetic images. By selecting synthetic images based on the confidence of their assigned labels and their feature similarity to real labeled images, our framework provides quality assurance to synthetic augmentation. Our models are evaluated on two datasets: a cervical histopathology image dataset with limited annotations, and another dataset of lymph node histopathology images with metastatic cancer. Here, we show that leveraging HistoGAN generated images with selective augmentation results in significant and consistent improvements of classification performance (6.7% and 2.8% higher accuracy, respectively) for cervical histopathology and metastatic cancer datasets.