# Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning

arXiv: https://arxiv.org/abs/2308.04578 · HTML: https://mlsanity.com/paper/2308.04578/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 34 of its 49 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2106.05095/) (2021)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [Deep neural network models for computational histopathology: A survey](https://mlsanity.com/paper/1912.12378/) (2019)
- [Deep Co-Training for Semi-Supervised Image Recognition](https://mlsanity.com/paper/1803.05984/) (2018)
- [PanNuke Dataset Extension, Insights and Baselines](https://mlsanity.com/paper/2003.10778/) (2020)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Collaborative Learning for Deep Neural Networks](https://mlsanity.com/paper/1805.11761/) (2018)
- [CoNIC: Colon Nuclei Identification and Counting Challenge 2022](https://mlsanity.com/paper/2111.14485/) (2021)

## Built on by (most cited first, 2 of 2)

- [Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2407.05352/) (2024)
- [Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging](https://mlsanity.com/paper/2405.06175/) (2024)

## Abstract (arXiv)

Automated semantic segmentation of cell nuclei in microscopic images is crucial for disease diagnosis and tissue microenvironment analysis. Nonetheless, this task presents challenges due to the complexity and heterogeneity of cells. While supervised deep learning methods are promising, they necessitate large annotated datasets that are time-consuming and error-prone to acquire. Semi-supervised approaches could provide feasible alternatives to this issue. However, the limited annotated data may lead to subpar performance of semi-supervised methods, regardless of the abundance of unlabeled data. In this paper, we introduce a novel unsupervised pre-training-based semi-supervised framework for cell-nuclei segmentation. Our framework is comprised of three main components. Firstly, we pretrain a diffusion model on a large-scale unlabeled dataset. The diffusion model's explicit modeling capability facilitates the learning of semantic feature representation from the unlabeled data. Secondly, we achieve semantic feature aggregation using a transformer-based decoder, where the pretrained diffusion model acts as the feature extractor, enabling us to fully utilize the small amount of labeled data. Finally, we implement a collaborative learning framework between the diffusion-based segmentation model and a supervised segmentation model to further enhance segmentation performance. Experiments were conducted on four publicly available datasets to demonstrate significant improvements compared to competitive semi-supervised segmentation methods and supervised baselines. A series of out-of-distribution tests further confirmed the generality of our framework. Furthermore, thorough ablation experiments and visual analysis confirmed the superiority of our proposed method.