Graph Neural Network for representation learning of lung cancer

BMC Cancer

Research

Juanjuan Zhao¹  Zijuan Zhao¹  Rukhma Aftab¹  Zia Urrehman¹  Yan Qiang¹ 

[1] College of Information and Computer, Taiyuan University of Technology, No. 79 Yingze West Street, 030024, Taiyuan, China;

关键词: Multiple instance learning;    Graph;    Whole slide images;    Graph neural networks;   

DOI  :  10.1186/s12885-023-11516-8

 received in 2023-05-16, accepted in 2023-10-11,  发布年份 2023

来源: Springer

【 摘 要 】

The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features.

【 授权许可】

CC BY   
© The Author(s) 2023

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