RegFSC-Net: Medical Image Registration via Fourier Transform With Spatial Reorganization and Channel Refinement Network.

Medical image registration is crucial in medical image analysis applications. Recently, U-Net-style networks have been commonly used for unsupervised image registration, predicting dense displacement fields in full-resolution space. However, this process is resource-intensive and time-consuming for high-resolution volumetric image data. To address this challenge, this paper proposes a novel model named RegFSC-Net, which utilizes Fourier transform with spatial reorganization (SR) and channel refinement (CR) network for registration. We embed efficient feature extraction modules SR and CR modules into the encoder, and adopt a parameter-free model to drive the decoder to improve the U-shaped network. Precisely, RegFSC-Net does not directly predict the full-resolution displacement field in space but learns the low-dimensional representation of the displacement field in the bandlimited Fourier domain, which is beneficial in reducing network parameters, memory usage, and computational costs. Experimental results show that RegFSC-Net outperforms various state-of-the-art methods. Specifically, in comparison to the widely recognized Transformer-based method TransMorph, RegFSC-Net utilizes only around 8.2% of its parameters, resulting in a 1.95% higher Dice score and significantly faster inference speeds of 126.67% and 419.99% on GPU and CPU, respectively. Furthermore, we also designed three variants of RegFSC-Net and demonstrated their potential applications in computer-aided diagnosis.

Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis.

Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-computer fusion. EEG signals have inherent temporal and spatial characteristics. However, existing studies did not fully consider the two properties. In addition, the position encoding mechanism in the vanilla transformer cannot effectively encode the continuous temporal character of the emotion. A temporal relative (TR) encoding mechanism is proposed to encode the temporal EEG signals for constructing the temporality self-attention in the transformer. To explore the contribution of each EEG channel corresponding to the electrode on the cerebral cortex to emotion analysis, a channel-attention (CA) mechanism is presented. The temporality self-attention mechanism cooperates with the channel-attention mechanism to utilize the temporal and spatial information of EEG signals simultaneously by preprocessing. Exhaustive experiments are conducted on the DEAP dataset, including the binary classification on valence, arousal, dominance, and liking. Furthermore, the discrete emotion category classification task is also conducted by mapping the dimensional annotations of DEAP into discrete emotion categories (5-class). Experimental results demonstrate that our model outperforms the advanced methods for all classification tasks.