A graph neural network model for the diagnosis of lung adenocarcinoma based on multimodal features and an edge-generation network.

ABSTRACT:

Background

Lung cancer is a global disease with high lethality, with early screening being considerably helpful for improving the 5-year survival rate. Multimodality features in early screening imaging are an important part of the prediction for lung adenocarcinoma, and establishing a model for adenocarcinoma diagnosis based on multimodal features is an obvious clinical need. Through our practice and investigation, we found that graph neural networks (GNNs) are excellent platforms for multimodal feature fusion, and the data can be completed using the edge-generation network. Therefore, we propose a new lung adenocarcinoma multiclassification model based on multimodal features and an edge-generation network.

Methods

According to a ratio of 80% to 20%, respectively, the datase

SUBMITTER: Li R 

PROVIDER: S-EPMC10423350 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature