A hierarchical algorithm with randomized learning for robust tissue segmentation and classification in digital pathology

Abstract

Highly detailed and accurate segmentation and classification of images constitutes an important class of tasks in computer vision. Typical “universal” domain-agnostic methods are known to suffer from instabilities and are prone to adversarial perturbations. Natural heterogeneity inherent in biological tissue structures complicates the interpretation of images even by trained physicians. Yet, algorithms in the medical domain require a high level of stability and interpretability to ensure their adoption by clinical experts and acceptance in clinical decision-making. In this work, we propose a novel method for segmentation and classification to address these challenges. The method is based on a hierarchical approach and biologically-informed feature extraction. The method's technical pipeline includes the automatic extraction of key biologically-informed features typically considered by physicians. This is followed by image classification using these features. Both stages rely on randomized ML techniques. The proposed hierarchical biomedically-informed approach significantly improved the image classification quality compared to the baseline solution of image classification in the task of colorectal cancer (CRC) analysis. The average F1-score for the four tissue types increased from 0.737 to 0.956. Using tumor tissue classification task as an example, we showed that the proposed algorithm offers an effective and practical avenue to solve these challenging issues.