病理学中，癌症分级对患者管理和治疗至关重要。最近基于卷积神经网络（CNN）的深度学习方法显示出了自动化和准确的癌症诊断的巨大潜力。然而，这些方法并未明确利用组织/细胞组成，因此难以结合现有癌症病理学知识。本研究提出了一种多细胞类型和多层级图聚合网络（MMGA-Net）用于癌症分级。给定一张病理图像，MMGA-Net在多个层级上构建多个细胞图，以表示细胞内和细胞间的关系，并融合全局和局部的细胞间相互作用。此外，它使用CNN提取组织的上下文信息。然后，组织和细胞信息被融合起来预测癌症分级。对两种癌症数据集的实验结果表明，MMGA-Net具有较好的性能，优于其他竞争模型。结果还表明，通过图的多细胞类型和多层级的信息融合对于改进病理图像分析至关重要。版权所有©2023年作者。由Elsevier B.V.出版。保留所有权利。

In pathology, cancer grading is crucial for patient management and treatment. Recent deep learning methods, based upon convolutional neural networks (CNNs), have shown great potential for automated and accurate cancer diagnosis. However, these do not explicitly utilize tissue/cellular composition, and thus difficult to incorporate the existing knowledge of cancer pathology. In this study, we propose a multi-cell type and multi-level graph aggregation network (MMGA-Net) for cancer grading. Given a pathology image, MMGA-Net constructs multiple cell graphs at multiple levels to represent intra- and inter-cell type relationships and to incorporate global and local cell-to-cell interactions. In addition, it extracts tissue contextual information using a CNN. Then, the tissue and cellular information are fused to predict a cancer grade. The experimental results on two types of cancer datasets demonstrate the effectiveness of MMGA-Net, outperforming other competing models. The results also suggest that the information fusion of multiple cell types and multiple levels via graphs is critical for improved pathology image analysis.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.