# Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network

arXiv: https://arxiv.org/abs/2208.02912 · HTML: https://mlsanity.com/paper/2208.02912/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- ["Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors](https://mlsanity.com/paper/1812.00467/) (2018)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (2019)
- [MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images](https://mlsanity.com/paper/1806.01963/) (2018)
- [Unsupervised Object Segmentation by Redrawing](https://mlsanity.com/paper/1905.13539/) (2019)
- [Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering](https://mlsanity.com/paper/2007.09990/) (2020)
- [W-Net: A Deep Model for Fully Unsupervised Image Segmentation](https://mlsanity.com/paper/1711.08506/) (2017)
- [Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images](https://mlsanity.com/paper/2007.05448/) (2020)
- [CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images](https://mlsanity.com/paper/2101.00442/) (2021)
- [Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting](https://mlsanity.com/paper/2005.02066/) (2020)
- [Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images](https://mlsanity.com/paper/2004.06689/) (2020)
- [Cross-Modality Deep Feature Learning for Brain Tumor Segmentation](https://mlsanity.com/paper/2201.02356/) (2022)
- [Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder](https://mlsanity.com/paper/1811.09655/) (2018)
- [Deep Conditional Gaussian Mixture Model for Constrained Clustering](https://mlsanity.com/paper/2106.06385/) (2021)
- [A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus images](https://mlsanity.com/paper/1701.00892/) (2017)
- [Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation](https://mlsanity.com/paper/2103.16344/) (2021)

## Built on by (most cited first, 6 of 6)

- [CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation](https://mlsanity.com/paper/2209.11359/) (2022)
- [Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation](https://mlsanity.com/paper/2402.16280/) (2024)
- [Revisiting Medical Image Retrieval via Knowledge Consolidation](https://mlsanity.com/paper/2503.09370/) (2025)
- [COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation](https://mlsanity.com/paper/2503.11439/) (2025)
- [Deep asymmetric mixture model for unsupervised cell segmentation](https://mlsanity.com/paper/2406.01815/) (2024)
- [Unsupervised Segmentation of Micro-CT Scans of Polyurethane Structures By Combining Hidden-Markov-Random Fields and a U-Net](https://mlsanity.com/paper/2511.11378/) (2025)

## Abstract (arXiv)

Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automatically segment structures in pathological images. Recently, automated machine and deep learning based methods dominate tissue segmentation research studies. However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixelwise annotations are expensive and sometimes can be impossible to obtain. This paper introduces a novel unsupervised learning paradigm by integrating an end-to-end deep mixture model with a constrained indicator to acquire accurate semantic tissue segmentation. This constraint aims to centralise the components of deep mixture models during the calculation of the optimisation function. In so doing, the redundant or empty class issues, which are common in current unsupervised learning methods, can be greatly reduced. By validation on both public and in-house datasets, the proposed deep constrained Gaussian network achieves significantly (Wilcoxon signed-rank test) better performance (with the average Dice scores of 0.737 and 0.735, respectively) on tissue segmentation with improved stability and robustness, compared to other existing unsupervised segmentation approaches. Furthermore, the proposed method presents a similar performance (p-value > 0.05) compared to the fully supervised U-Net.