Multiscale Multimodal Medical Imaging

Multi-Modal Image Prediction via Spatial Hybrid U-Net.- Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network.- OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical Images.- Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data.- Feature Pyramid based Attention for Cervical Image Classification.- Single-scan Dual-tracer Separation Network Based on Pre-trained GRU.- PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation.- Automated Classification of Arterioles and Venules for Retina Fundus Images using Dual Deeply-Supervised Network.- Liver Segmentation from Multimodal Images using HED-Mask R-CNN.- aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection.- Speckle Noise Removal in Ultrasound Images Using A Deep Convolutional Neural Network and A Specially Designed Loss Function.- Automatic Sinus Surgery Skill Assessment Based on Instrument Segmentation and Tracking in Endoscopic Video.- U-Net Training with Instance-Layer Normalization.