在病理图像分析中，癌症分级是一个重要的任务，因为它对患者的护理、治疗和管理至关重要。最近人工神经网络在计算病理学领域的发展显示出提高癌症诊断准确性和质量的巨大潜力。这些改进通常归因于网络结构的进展，往往导致计算与资源的增加。在本研究中，我们提出了一种高效的卷积神经网络，通过度量学习准确而鲁棒地进行多类癌症分类。 我们提出了一种适用于病理图像中改进癌症分级的中心感知度量学习网络。所提出的网络利用特征嵌入空间中不同类别的中心点来优化病理图像之间的相对距离，从而显示它们之间的固有相似性/差异性。为了改进优化，我们引入了一种新的损失函数和训练策略，这些方法是针对所提出的网络和度量学习进行量身定制的。 我们在多个结直肠癌和胃癌数据集上评估了所提出的方法。对于结直肠癌，我们使用了两个不同的数据集，这些数据集是从不同的获取设置中收集的。提出的方法在第一个数据集中的准确率、F1分数和二次加权kappa值分别为88.7％、0.849和0.946，在第二个数据集中的准确率、F1分数和二次加权kappa值分别为83.3％、0.764和0.907。对于胃癌，所提出的方法获得了85.9％的准确率、0.793的F1分数和0.939的二次加权kappa值。我们还发现，所提出的方法在性能上优于其他竞争模型，并且具有较高的计算效率。 实验结果表明，所提出网络的预测结果既准确又可靠。所提出的网络不仅在癌症分类中优于其他相关方法，而且在训练和推断过程中实现了更高的计算效率。未来的研究将进一步发展所提出的方法，并将该方法应用于其他问题和领域。 版权所有© 2023 作者。由Elsevier B.V.出版。保留所有权利。

Cancer grading in pathology image analysis is a major task due to its importance in patient care, treatment, and management. The recent developments in artificial neural networks for computational pathology have demonstrated great potential to improve the accuracy and quality of cancer diagnosis. These improvements are generally ascribable to the advance in the architecture of the networks, often leading to increase in the computation and resources. In this work, we propose an efficient convolutional neural network that is designed to conduct multi-class cancer classification in an accurate and robust manner via metric learning.We propose a centroid-aware metric learning network for an improved cancer grading in pathology images. The proposed network utilizes centroids of different classes within the feature embedding space to optimize the relative distances between pathology images, which manifest the innate similarities/dissimilarities between them. For improved optimization, we introduce a new loss function and a training strategy that are tailored to the proposed network and metric learning.We evaluated the proposed approach on multiple datasets of colorectal and gastric cancers. For the colorectal cancer, two different datasets were employed that were collected from different acquisition settings. the proposed method achieved an accuracy, F1-score, quadratic weighted kappa of 88.7%, 0.849, and 0.946 for the first dataset and 83.3%, 0.764, and 0.907 for the second dataset, respectively. For the gastric cancer, the proposed method obtained an accuracy of 85.9%, F1-score of 0.793, and quadratic weighted kappa of 0.939. We also found that the proposed method outperforms other competing models and is computationally efficient.The experimental results demonstrate that the prediction results by the proposed network are both accurate and reliable. The proposed network not only outperformed other related methods in cancer classification but also achieved superior computational efficiency during training and inference. The future study will entail further development of the proposed method and the application of the method to other problems and domains.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.