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.

翻译：癌症分级是病理学中的一项关键任务。近年来，人工神经网络在计算病理学领域的发展表明，这些方法在提升癌症诊断准确性与质量方面具有巨大潜力。然而，此类方法的稳健性和可靠性问题尚未完全解决。为此，本文提出一种质心感知特征重校准网络，能够以高精度和强鲁棒性实现癌症分级。该网络将输入病理图像映射至嵌入空间，并通过注意力机制，利用不同癌症分级对应的质心嵌入向量对映射结果进行调整。借助重校准后的嵌入向量，所提网络将输入病理图像分类至相应的类别标签（即癌症分级）。我们采用不同环境下采集的结直肠癌数据集对所提网络进行评估。实验结果证实，无论数据集的环境如何变化，该网络均能以高准确率完成病理图像的癌症分级任务。