# SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images

arXiv: https://arxiv.org/abs/2003.05712 · HTML: https://mlsanity.com/paper/2003.05712/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Computational Cytology: A Survey](https://mlsanity.com/paper/2202.05126/) (2022)
- [Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection](https://mlsanity.com/paper/2106.04919/) (2021)
- [Could We Generate Cytology Images from Histopathology Images? An Empirical Study](https://mlsanity.com/paper/2403.10885/) (2024)

## Abstract (arXiv)

One of the most challenging aspects of medical image analysis is the lack of a high quantity of annotated data. This makes it difficult for deep learning algorithms to perform well due to a lack of variations in the input space. While generative adversarial networks have shown promise in the field of synthetic data generation, but without a carefully designed prior the generation procedure can not be performed well. In the proposed approach we have demonstrated the use of automatically generated segmentation masks as learnable class-specific priors to guide a conditional GAN for the generation of patho-realistic samples for cytology image. We have observed that augmentation of data using the proposed pipeline called "SynCGAN" improves the performance of state of the art classifiers such as ResNet-152, DenseNet-161, Inception-V3 significantly.