Year: 2024
Type: article
Abstract: Due to the irregular shape and large difference of lung tumors in 3D medical images, the feature extraction of lesions is insufficient, and the recognition accuracy is not high, a 3D Cross-Modal ConvFormer is proposed. Firstly, three networks are utilized to learn the 3D PET, CT and PET/CT medical images. Secondly, a ConvFormer model is designed to fuse global and shallow local features, while self-correcting convolution expands the receptive field for better lesion extraction. Finally, a dual-branch cross-modal feature interaction block is designed to enhance cross-modal features and capture 3D multimodal details. This module uses two global attention mechanisms to improve the extraction of cross-modal and global-local information. The experiments use a 3D multimodal lung tumor dataset with 3 173 patients. With optimized parameters and computation time, the 3D-CConvFormer achieves an accuracy of 89.25% and an AUC of 88.74%, providing reliable computer-aided diagnosis for 3D multimodal lung tumor.
Authors: Tao Zhou, Xinyu Ye, Fengzhen Liu, Huiling Lu
Institutions: North Minzu University, Ningxia Medical University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4416340355
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