Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology

Fuyao Chen, Yuexi Du, Tal Zeevi, Nicha C Dvornek, John A Onofrey

Keywords: Histopathology, Unsupervised Segmentation, Equivariance, Convolutional Neural Network, Prostate cancer

TL;DR: This study introduces unsupervised segmentation for prostate cancer histopathology using rotation-invariant biomarkers and a novel symmetric convolutional kernel, enhancing the accuracy and robustness of digital pathology analysis.

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.

Primary Subject Area: Unsupervised Learning and Representation Learning

Secondary Subject Area: Application: Histopathology

Paper Type: Both

Registration Requirement: Yes

Visa & Travel: Yes

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Submission Number: 122