Four-phase CT lesion recognition based on multi-phase information fusion framework and spatiotemporal prediction module

2024

BioMedical Engineering OnLine, Vol 23, Iss 1, Pp 1-18 (2024)

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.

article

English

BMC, 2024.

Deep learning, Contrast-enhanced CT, Hepatic malignancy, Classification, Artificial intelligence, Medical technology, R855-855.5