Abstract
Lung cancer is one of the most widely spread cancers in the world. So far, the histopathological image remains the “gold standard” in diagnosing lung cancers, and multiple types of pathological images features have been associated with lung cancer diagnosis and progression. However, most of the existing studies only utilized single type of image features, which did not take advantages of multiple types of image features. In this paper, we propose a Block based Multi-View Graph Convolutional Network (i.e., BMVGCN), which integrates multiple types of image features from histopathological images for lung cancer diagnosis. Specifically, our method utilizes the block-based bilinear combination model to fuse different types of features. By considering the correlation among different samples, we also introduce the Graph Convolutional Network to exploit the correlations among samples that could lead to better diagnosis performance. To evaluate the effectiveness of the proposed method, we conduct the experiments for the classification of the cancer tissue and non-cancer tissue in both Lung Adenocarcinoma (i.e., LUAD) and Lung Squamous Cell Carcinoma (i.e.,LUSC), and the discrimination between LUAD and LUSC. The results show that our method can achieve superior classification performance than the comparing methods.
This work was supported by National Natural Science Foundation of China (Nos. 62136004, 61902183, 62106104), the National Key R &D Program of China (Grant Nos.: 2018YFC2001600, 2018YFC2001602 and 2018YFA0701703), and the Project funded by China Postdoctoral Science Foundation (No. 2022T150320).
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Pei, Z., Zuo, Y., Sun, L., Wang, M., Zhang, D., Shao, W. (2022). Integrative Analysis of Multi-view Histopathological Image Features for the Diagnosis of Lung Cancer. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_47
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