  Artificial intelligence-enabled histopathological data analysis has become a
valuable assistant to the pathologist. However, existing models lack
representation and inference abilities compared with those of pathologists,
especially in cancer subtype diagnosis, which is unconvincing in clinical
practice. For instance, pathologists typically observe the lesions of a slide
from global to local, and then can give a diagnosis based on their knowledge
and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K)
model to replicate the process of cancer subtype classification on a
histopathological slide like a pathologist. Specifically, in the data-driven
module, the bagging mechanism in ensemble learning is leveraged to integrate
the histological features from various bags extracted by the embedding
representation unit. Furthermore, a knowledge-driven module is established
based on the Gestalt principle in psychology to build the three-dimensional
(3D) expert knowledge space and map histological features into this space for
metric. Then, the diagnosis can be made according to the Euclidean distance
between them. Extensive experimental results on both public and in-house
datasets demonstrate that the D&K model has a high performance and credible
results compared with the state-of-the-art methods for diagnosing
histopathological subtypes. Code:
https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classification
