# Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images

arXiv: https://arxiv.org/abs/2101.11600 · HTML: https://mlsanity.com/paper/2101.11600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium](https://mlsanity.com/paper/1706.08500/) (2017)
- [3D Shape Induction from 2D Views of Multiple Objects](https://mlsanity.com/paper/1612.05872/) (2016)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification](https://mlsanity.com/paper/1907.10655/) (2019)
- [Generative Image Translation for Data Augmentation in Colorectal Histopathology Images](https://mlsanity.com/paper/1910.05827/) (2019)
- [Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy](https://mlsanity.com/paper/1906.01529/) (2019)
- [Unsupervised Histopathology Image Synthesis](https://mlsanity.com/paper/1712.05021/) (2017)
- [Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks](https://mlsanity.com/paper/1612.03217/) (2016)

## Built on by (most cited first, 1 of 1)

- [Neuroplastic graph attention networks for nuclei segmentation in histopathology images](https://mlsanity.com/paper/2201.03669/) (2022)

## Abstract (arXiv)

Synthetic generation of three-dimensional cell models from histopathological images aims to enhance understanding of cell mutation, and progression of cancer, necessary for clinical assessment and optimal treatment. Classical reconstruction algorithms based on image registration of consecutive slides of stained tissues are prone to errors and often not suitable for the training of three-dimensional segmentation algorithms. We propose a novel framework to generate synthetic three-dimensional histological models based on a generator-discriminator pattern optimizing constrained features that construct a 3D model via a Blender interface exploiting smooth shape continuity typical for biological specimens. To capture the spatial context of entire cell clusters we deploy a novel deep topology transformer that implements and attention mechanism on cell group images to extract features for the frozen feature decoder. The proposed algorithms achieves high quantitative and qualitative synthesis evident in comparative evaluation metrics such as a low Frechet-Inception scores.