  Cancer grading is an essential task in pathology. The recent developments of
artificial neural networks in computational pathology have shown that these
methods hold great potential for improving the accuracy and quality of cancer
diagnosis. However, the issues with the robustness and reliability of such
methods have not been fully resolved yet. Herein, we propose a centroid-aware
feature recalibration network that can conduct cancer grading in an accurate
and robust manner. The proposed network maps an input pathology image into an
embedding space and adjusts it by using centroids embedding vectors of
different cancer grades via attention mechanism. Equipped with the recalibrated
embedding vector, the proposed network classifiers the input pathology image
into a pertinent class label, i.e., cancer grade. We evaluate the proposed
network using colorectal cancer datasets that were collected under different
environments. The experimental results confirm that the proposed network is
able to conduct cancer grading in pathology images with high accuracy
regardless of the environmental changes in the datasets.
