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

• URL: http://arxiv.org/abs/2007.03292v1
• Date: Tue, 7 Jul 2020 09:15:36 GMT
• Title: Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
• Authors: Christian Abbet, and Inti Zlobec, and Behzad Bozorgtabar, and Jean-Philippe Thiran
• Abstract summary: We propose a self-supervised learning method that learns a representation of tissue regions as well as a metric of the clustering to obtain their underlying patterns. We show that the proposed approach can benefit from linear predictors to avoid overfitting in patient outcomes predictions.
• Score: 9.431791041887957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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