Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression

TL;DR

This paper introduces a probabilistic deep learning ordinal regression method for accurately estimating prostate cancer Gleason scores from whole-slide images, enhancing interpretability and performance over traditional approaches.

Contribution

It presents a novel deep quantum measurement-based ordinal regression model specifically designed for prostate tissue grading from WSIs, improving accuracy and interpretability.

Findings

Outperforms conventional deep classification models in accuracy.

Provides more interpretable Gleason score estimations.

Demonstrates effectiveness on prostate whole-slide images.

Abstract

Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity and treatment to follow. The pathologist looks at the arrangement of cancer cells in the prostate and assigns a score on a scale that ranges from 6 to 10. Automatic analysis of prostate whole-slide images (WSIs) is usually addressed as a binary classification problem, which misses the finer distinction between stages given by the GS. This paper presents a probabilistic deep learning ordinal classification method that can estimate the GS from a prostate WSI. Approaching the problem as an ordinal regression task using a differentiable probabilistic model not only improves the interpretability of the results, but also improves the accuracy of the model when compared to conventional deep classification and regression architectures.