# Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification

arXiv: https://arxiv.org/abs/1907.10655 · HTML: https://mlsanity.com/paper/1907.10655/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 17 of 17)

- [Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification](https://mlsanity.com/paper/2111.06399/) (2021)
- [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022)
- [Synthetic Augmentation with Large-scale Unconditional Pre-training](https://mlsanity.com/paper/2308.04020/) (2023)
- [TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation](https://mlsanity.com/paper/2107.09843/) (2021)
- [Semi-supervised Body Parsing and Pose Estimation for Enhancing Infant General Movement Assessment](https://mlsanity.com/paper/2210.08054/) (2022)
- [Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation](https://mlsanity.com/paper/2110.00404/) (2021)
- [Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images](https://mlsanity.com/paper/2205.15530/) (2022)
- [DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning](https://mlsanity.com/paper/2408.14584/) (2024)
- [Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data](https://mlsanity.com/paper/2504.12351/) (2025)
- [Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images](https://mlsanity.com/paper/2101.11600/) (2021)
- [Neuroplastic graph attention networks for nuclei segmentation in histopathology images](https://mlsanity.com/paper/2201.03669/) (2022)
- [Deepfake histological images for enhancing digital pathology](https://mlsanity.com/paper/2206.08308/) (2022)
- [Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review](https://mlsanity.com/paper/2410.19820/) (2024)
- [Analyzing the effectiveness of image augmentations for face recognition from limited data](https://mlsanity.com/paper/2105.08796/) (2021)
- [Recent trends and analysis of Generative Adversarial Networks in Cervical Cancer Imaging](https://mlsanity.com/paper/2209.12680/) (2022)
- [End to End Generative Meta Curriculum Learning For Medical Data Augmentation](https://mlsanity.com/paper/2212.10086/) (2022)
- [Data Efficient Contrastive Learning in Histopathology using Active Sampling](https://mlsanity.com/paper/2303.16247/) (2023)

## Abstract (arXiv)

Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a crucial indicator in cervical biopsy results. Accurate CIN grading of epithelium regions helps pathologists with precancerous lesion diagnosis and treatment planning. Although an automated CIN grading system has been desired, supervised training of such a system would require a large amount of expert annotations, which are expensive and time-consuming to collect. In this paper, we investigate the CIN grade classification problem on segmented epithelium patches. We propose to use conditional Generative Adversarial Networks (cGANs) to expand the limited training dataset, by synthesizing realistic cervical histopathology images. While the synthetic images are visually appealing, they are not guaranteed to contain meaningful features for data augmentation. To tackle this issue, we propose a synthetic-image filtering mechanism based on the divergence in feature space between generated images and class centroids in order to control the feature quality of selected synthetic images for data augmentation. Our models are evaluated on a cervical histopathology image dataset with a limited number of patch-level CIN grade annotations. Extensive experimental results show a significant improvement of classification accuracy from 66.3% to 71.7% using the same ResNet18 baseline classifier after leveraging our cGAN generated images with feature-based filtering, which demonstrates the effectiveness of our models.