HIGHLIGHTS
SUMMARY
The CSAM is an end-to-end generic module that can be seamlessly integrated into three-dimensional convolutional neural_networks. Since the convolutional neural_networks are suitable for extracting local features, the authors combine the Branch-Fusion Net and the CSAM as the local feature extraction network and use the Bi-Directional Gated Recurrent Unit (Bi-GRU) for the global feature extraction. Adding the CSAM to the mainstream 3D convolutional neural_networks can significantly improve the feature extraction effect. Lu et_al combine the Variational Auto-Encoder (VAE) with the Conv-LSTM to propose the Convolutional Variational Recurrent . . .
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