1. Four-phase CT lesion recognition based on multi-phase information fusion framework and spatiotemporal prediction module.
- Author
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Qiao, Shaohua, Xue, Mengfan, Zuo, Yan, Zheng, Jiannan, Jiang, Haodong, Zeng, Xiangai, and Peng, Dongliang
- Subjects
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ARTIFICIAL intelligence , *DATA augmentation , *DEEP learning , *COMPUTED tomography , *ALGORITHMS - Abstract
Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features. In the spatiotemporal prediction module, we design a 1D deep residual network to integrate multiphase feature vectors, and use the GRU architecture to model the temporal enhancement information between CT slices. In addition, we employ CT image pseudo-color processing for data augmentation and train the whole network based on a multi-task learning framework. We verify the proposed network on a four-phase CT dataset. The experimental results show that the proposed network can effectively fuse the multi-phase information and model the temporal enhancement information between CT slices, showing excellent performance in lesion recognition. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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