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.

应用深度学习技术分析组织病理图片对于自动预后预测的重要性进行了创新性研究，提出了一个新颖的三部分框架，包括基于卷积网络的组织分割算法、对比学习模块和嵌套多示例学习分类模块。在人工生成的数据和简单的诊断任务上进行了初步验证，并在膀胱癌的预后预测任务中取得了显著的成果，模型的 AUC 分别达到 0.721 和 0.678。