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.

提出了一种能够以准确和稳健的方式进行癌症分级的重心感知特征校准网络，通过注意机制利用不同癌症等级的重心嵌入向量将输入的病理图像映射到嵌入空间并进行调整，然后使用重新校准的嵌入向量对输入的病理图像进行恰当的分类标签，实验证实该网络能够在不同环境下高精度地进行病理图像中的癌症分级。