NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning

TL;DR

This paper presents NMGrad, a weakly supervised deep learning pipeline for bladder cancer grading from histological slides, improving accuracy and explainability over previous methods.

Contribution

Introduction of a novel nested multiple instance learning approach with attention for bladder cancer grading using weakly labeled histological data.

Findings

Model outperforms previous state-of-the-art methods.

Attention scores correlate with high-grade regions.

Provides explainability in histopathological grading.

Abstract

The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime factor for patient risk stratification, although it suffers from inconsistencies and variations among pathologists. Moreover, absence of annotations in medical imaging difficults training deep learning models. To address these challenges, we introduce a pipeline designed for bladder cancer grading using histological slides. First, it extracts urothelium tissue tiles at different magnification levels, employing a convolutional neural network for processing for feature extraction. Then, it engages in the slide-level prediction process. It employs a nested multiple instance learning approach with attention to predict the grade. To distinguish different levels of malignancy within specific regions of the slide, we include the origins of the tiles in our analysis. The attention scores at region level is shown to correlate with verified high-grade regions, giving some explainability to the model. Clinical evaluations demonstrate that our model consistently outperforms previous state-of-the-art methods.