  The whole slide histopathology images (WSIs) play a critical role in gastric
cancer diagnosis. However, due to the large scale of WSIs and various sizes of
the abnormal area, how to select informative regions and analyze them are quite
challenging during the automatic diagnosis process. The multi-instance learning
based on the most discriminative instances can be of great benefit for whole
slide gastric image diagnosis. In this paper, we design a recalibrated
multi-instance deep learning method (RMDL) to address this challenging problem.
We first select the discriminative instances, and then utilize these instances
to diagnose diseases based on the proposed RMDL approach. The designed RMDL
network is capable of capturing instance-wise dependencies and recalibrating
instance features according to the importance coefficient learned from the
fused features. Furthermore, we build a large whole-slide gastric
histopathology image dataset with detailed pixel-level annotations.
Experimental results on the constructed gastric dataset demonstrate the
significant improvement on the accuracy of our proposed framework compared with
other state-of-the-art multi-instance learning methods. Moreover, our method is
general and can be extended to other diagnosis tasks of different cancer types
based on WSIs.
