Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology

TL;DR

This paper introduces a novel equivariant imaging biomarker approach for unsupervised segmentation in histopathology, enhancing robustness to rotation and improving generalization in digital pathology analysis.

Contribution

It presents a new symmetric convolutional kernel method for developing equivariant biomarkers, validated on prostate tissue images, improving robustness and generalizability of ML models.

Findings

Enhanced robustness to rotation in histopathological segmentation

Improved generalization of biomarkers across different orientations

Validated on prostate tissue micro-array images from 50 patients

Abstract

Histopathology evaluation of tissue specimens through microscopic examination is essential for accurate disease diagnosis and prognosis. However, traditional manual analysis by specially trained pathologists is time-consuming, labor-intensive, cost-inefficient, and prone to inter-rater variability, potentially affecting diagnostic consistency and accuracy. As digital pathology images continue to proliferate, there is a pressing need for automated analysis to address these challenges. Recent advancements in artificial intelligence-based tools such as machine learning (ML) models, have significantly enhanced the precision and efficiency of analyzing histopathological slides. However, despite their impressive performance, ML models are invariant only to translation, lacking invariance to rotation and reflection. This limitation restricts their ability to generalize effectively, particularly in histopathology, where images intrinsically lack meaningful orientation. In this study, we develop robust, equivariant histopathological biomarkers through a novel symmetric convolutional kernel via unsupervised segmentation. The approach is validated using prostate tissue micro-array (TMA) images from 50 patients in the Gleason 2019 Challenge public dataset. The biomarkers extracted through this approach demonstrate enhanced robustness and generalizability against rotation compared to models using standard convolution kernels, holding promise for enhancing the accuracy, consistency, and robustness of ML models in digital pathology. Ultimately, this work aims to improve diagnostic and prognostic capabilities of histopathology beyond prostate cancer through equivariant imaging.