NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
This work addresses grading inconsistencies for bladder cancer patients, though it is incremental as it builds on existing weakly supervised methods in medical imaging.
The paper tackled the problem of inconsistent and annotation-scarce bladder cancer grading from histopathological slides by introducing a weakly supervised deep learning pipeline, achieving state-of-the-art performance in clinical evaluations.
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.