# Enhanced Sharp-GAN For Histopathology Image Synthesis

arXiv: https://arxiv.org/abs/2301.10187 · HTML: https://mlsanity.com/paper/2301.10187/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 24 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Methods for Segmentation and Classification of Digital Microscopy Tissue Images](https://mlsanity.com/paper/1810.13230/) (2018)
- [Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/2002.01020/) (2020)
- [Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis](https://mlsanity.com/paper/2110.14709/) (2021)

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

- [PathDiff: Histopathology Image Synthesis with Unpaired Text and Mask Conditions](https://mlsanity.com/paper/2506.23440/) (2025)
- [DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading](https://mlsanity.com/paper/2404.13097/) (2024)
- [Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis](https://mlsanity.com/paper/2308.09835/) (2023)

## Abstract (arXiv)

Histopathology image synthesis aims to address the data shortage issue in training deep learning approaches for accurate cancer detection. However, existing methods struggle to produce realistic images that have accurate nuclei boundaries and less artifacts, which limits the application in downstream tasks. To address the challenges, we propose a novel approach that enhances the quality of synthetic images by using nuclei topology and contour regularization. The proposed approach uses the skeleton map of nuclei to integrate nuclei topology and separate touching nuclei. In the loss function, we propose two new contour regularization terms that enhance the contrast between contour and non-contour pixels and increase the similarity between contour pixels. We evaluate the proposed approach on the two datasets using image quality metrics and a downstream task (nuclei segmentation). The proposed approach outperforms Sharp-GAN in all four image quality metrics on two datasets. By integrating 6k synthetic images from the proposed approach into training, a nuclei segmentation model achieves the state-of-the-art segmentation performance on TNBC dataset and its detection quality (DQ), segmentation quality (SQ), panoptic quality (PQ), and aggregated Jaccard index (AJI) is 0.855, 0.863, 0.691, and 0.683, respectively.