Structure Preserving Cycle-Gan for Unsupervised Medical Image Domain Adaptation.

The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. This work introduces the Structure Preserving Cycle-GAN (SP Cycle-GAN) for unsupervised domain adaptation (DA) of segmentation datasets, which promotes medical structure preservation during image translation through the enforcement of a segmentation loss term in the overall Cycle-GAN training process. We demonstrate the structure preserving capability of the SP Cycle-GAN both visually and through comparison of Dice score segmentation performance for the un-supervised domain adaptation models.

Multi-Modal Emotion Recognition Using EEG and Eye Tracking Features.

Multi-modal emotion recognition from various human physiological indicators has emerged as a large topic of interest, including the use of EEG, ECG, GSR and Eye Tracking features. This work introduced a simple CNN based multi-modal EEG and Eye Tracking emotion recognition model for the SEED V dataset. In contrast to other works on the SEED V dataset, different Differential Entropy time windows were tested for EEG feature extraction.