Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency
Graph neural networks could compare the difference among all samples (nodes in graph) and transmit the interrelationship among them to obtain a global landscape. Compared with radiomics and clinical feature‐based machine learning methods, whether a graph convolutional neural network (GCNN) based on...
Saved in:
| Main Authors: | Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-08-01
|
| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400874 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Graph convolutional network model with a feature compensation module and dual-channel second-order pooling module for multimodal emotion recognition in conversation
by: Xiaocong Tan, et al.
Published: (2025-07-01) -
Lung Nodule Classification Based on 3D Convolutional Neural Network
by: WANG Wei-bing, et al.
Published: (2021-08-01) -
DCAI: a dual cross-attention integration framework for benign-malignant classification of pulmonary nodules
by: Shuling Wang, et al.
Published: (2025-07-01) -
MM-HGNN: Multimodal Representation Learning Heterogeneous Graph Neural Network
by: Khalil Bachiri, et al.
Published: (2025-07-01) -
Management and Outcomes of Pulmonary Nodules in a Real-World Setting
by: Berta Mosleh, et al.
Published: (2025-07-01)