Multiscale Multimodal Medical Imaging : First International Workshop, MMMI 2019, Held in Conjunction with Miccai 2019, Shenzhen, China, October 13, 201
Overview
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.
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Details
- ISBN-13: 9783030379681
- ISBN-10: 303037968X
- Publisher: Springer
- Publish Date: December 2019
- Dimensions: 9.21 x 6.14 x 0.25 inches
- Shipping Weight: 0.39 pounds
- Page Count: 109
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