Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma,as well as related histology and molecular markers on a multi-institutional dataset.

翻译：近期，癌症病理诊断正转向整合组织学特征与分子标志物。数字病理学方法亟需有效融合分子标志物与组织学信息，以在实际场景中实现更精准的诊断。本文首次尝试基于全切片图像，通过联合预测分子标志物与组织学特征并建模其交互关系，实现弥漫性胶质瘤分类。具体而言，我们提出一种分层多任务多实例学习框架，以联合预测组织学特征与分子标志物。此外，我们设计了一种基于共现概率的标签修正图网络，用于建模分子标志物的共现模式。最后，我们提出一种动态置信度约束损失下的跨组学交互策略，以建模组织学与分子标志物之间的相互作用。实验结果表明，在多机构数据集上，我们的方法在弥漫性胶质瘤分类及相关组织学与分子标志物预测任务中优于现有最先进方法。