# Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer

arXiv: https://arxiv.org/abs/2007.03292 · HTML: https://mlsanity.com/paper/2007.03292/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 17 of 17)

- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [How Transferable Are Self-supervised Features in Medical Image Classification Tasks?](https://mlsanity.com/paper/2108.10048/) (2021)
- [Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images](https://mlsanity.com/paper/2202.07001/) (2022)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image](https://mlsanity.com/paper/2306.17373/) (2023)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection](https://mlsanity.com/paper/2108.09178/) (2021)
- [Topology-Guided Multi-Class Cell Context Generation for Digital Pathology](https://mlsanity.com/paper/2304.02255/) (2023)
- [SLPD: Slide-level Prototypical Distillation for WSIs](https://mlsanity.com/paper/2307.10696/) (2023)
- [Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification](https://mlsanity.com/paper/2305.02032/) (2023)
- [GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation](https://mlsanity.com/paper/2309.01487/) (2023)
- [EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting](https://mlsanity.com/paper/2101.11085/) (2021)
- [Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study](https://mlsanity.com/paper/2107.07761/) (2021)
- [Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning](https://mlsanity.com/paper/2407.14904/) (2024)
- [Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering](https://mlsanity.com/paper/2204.01593/) (2022)
- [SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images](https://mlsanity.com/paper/2505.06710/) (2025)

## Abstract (arXiv)

With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is usually limited to only a few histopathological features. However, most of the tumor microenvironments used to describe patterns of aggressive tumor behavior are ignored. In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To do so, we propose a self-supervised learning method that jointly learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. These histopathological patterns are then used to represent the interaction between complex tissues and predict clinical outcomes directly. We furthermore show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information. Histomorphological clusters obtained by our method are evaluated by training survival models. The experimental results demonstrate statistically significant patient stratification, and our approach outperformed the state-of-the-art deep clustering methods.