  Whole slide image (WSI) has been widely used to assist automated diagnosis
under the deep learning fields. However, most previous works only discuss the
SINGLE task setting which is not aligned with real clinical setting, where
pathologists often conduct multiple diagnosis tasks simultaneously. Also, it is
commonly recognized that the multi-task learning paradigm can improve learning
efficiency by exploiting commonalities and differences across multiple tasks.
To this end, we present a novel multi-task framework (i.e., MulGT) for WSI
analysis by the specially designed Graph-Transformer equipped with Task-aware
Knowledge Injection and Domain Knowledge-driven Graph Pooling modules.
Basically, with the Graph Neural Network and Transformer as the building
commons, our framework is able to learn task-agnostic low-level local
information as well as task-specific high-level global representation.
Considering that different tasks in WSI analysis depend on different features
and properties, we also design a novel Task-aware Knowledge Injection module to
transfer the task-shared graph embedding into task-specific feature spaces to
learn more accurate representation for different tasks. Further, we elaborately
design a novel Domain Knowledge-driven Graph Pooling module for each task to
improve both the accuracy and robustness of different tasks by leveraging
different diagnosis patterns of multiple tasks. We evaluated our method on two
public WSI datasets from TCGA projects, i.e., esophageal carcinoma and kidney
carcinoma. Experimental results show that our method outperforms single-task
counterparts and the state-of-theart methods on both tumor typing and staging
tasks.
