Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medical implications. However, limited by their shallowness and suboptimal use of high-dimensional pixel data, GCNs underperform in multi-class histopathological image classification. To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification. To improve the explainability of the entire framework by embedding morphological and topological distribution of cells, we build a 14-layer deep graph convolutional network to handle cell graph data. For the further utilization and dynamic interactions between pixel-level and cell-level information, we also design a co-training strategy to integrate the two asymmetric branches. Notably, we collect a private clinically acquired dataset termed LUAD7C, including seven subtypes of lung adenocarcinoma, which is rare and more challenging. We evaluated our approach on the private LUAD7C and public colorectal cancer datasets, showcasing its superior performance, explainability, and generalizability in multi-class histopathological image classification.

翻译：卷积神经网络在组织病理图像分类中表现出色，但其像素级关注点限制了可解释性。相反，新兴的图卷积网络聚焦于细胞级特征及其医学意义。然而，受限于网络深度不足及对高维像素数据的次优利用，图卷积网络在多类组织病理图像分类中表现欠佳。为动态充分利用像素级与细胞级特征，我们提出了一种非对称协同训练框架，该框架结合了深度图卷积网络与卷积神经网络，用于多类组织病理图像分类。为通过嵌入细胞的形态与拓扑分布提升整体框架的可解释性，我们构建了一个14层深度图卷积网络来处理细胞图数据。为进一步利用像素级与细胞级信息并实现二者间的动态交互，我们还设计了一种协同训练策略来整合这两个非对称分支。值得注意的是，我们收集了一个名为LUAD7C的私有临床数据集，包含肺腺癌的七种亚型，该数据集罕见且更具挑战性。我们在私有LUAD7C数据集及公开结直肠癌数据集上评估了所提方法，展示了在多类组织病理图像分类中的优越性能、可解释性及泛化能力。