  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.
