  We present a pioneering investigation into the application of deep learning
techniques to analyze histopathological images for addressing the substantial
challenge of automated prognostic prediction. Prognostic prediction poses a
unique challenge as the ground truth labels are inherently weak, and the model
must anticipate future events that are not directly observable in the image. To
address this challenge, we propose a novel three-part framework comprising of a
convolutional network based tissue segmentation algorithm for region of
interest delineation, a contrastive learning module for feature extraction, and
a nested multiple instance learning classification module. Our study explores
the significance of various regions of interest within the histopathological
slides and exploits diverse learning scenarios. The pipeline is initially
validated on artificially generated data and a simpler diagnostic task.
Transitioning to prognostic prediction, tasks become more challenging.
Employing bladder cancer as use case, our best models yield an AUC of 0.721 and
0.678 for recurrence and treatment outcome prediction respectively.
