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Medical Image Computing and Computer Assisted Intervention - Miccai 2019|Dinggang Shen

Medical Image Computing and Computer Assisted Intervention - Miccai 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceed

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Overview

Image Segmentation.- Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation.- Comparative Evaluation of Hand-Engineered and Deep-Learned Features for Neonatal Hip Bone Segmentation in Ultrasound.- Unsupervised Quality Control of Image Segmentation based on Bayesian Learning.- One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation.- 'Project & Excite' Modules for Segmentation of Volumetric Medical Scans.- Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation.- Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation.- Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network.- Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation.- Instance Segmentation from Volumetric Biomedical Images without Voxel-Wise Labeling.- Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice.- Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation.- HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images.- PHiSeg: Capturing Uncertainty in Medical Image Segmentation.- Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data.- Supervised Uncertainty Quantification for Segmentation with Multiple Annotations.- 3D Tiled Convolution for Effective Segmentation of Volumetric Medical Images.- Hyper-Pairing Network for Multi-Phase Pancreatic Ductal Adenocarcinoma Segmentation.- Statistical intensity- and shape-modeling to automate cerebrovascular segmentation from TOF-MRA data.- Segmentation of Vessels in Ultra High Frequency Ultrasound Sequences using Contextual Memory.- Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion.- Mixed-Supervised Dual-Network for Medical Image Segmentation.- Fully Automated Pancreas Segmentation with Two-stage 3D Convolutional Neural Networks.- Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation.- Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images.- Resource Optimized Neural Architecture Search for 3D Medical Image Segmentation.- Radiomics-guided GAN for Segmentation of Liver Tumor without Contrast Agents.- Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks.- Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation.- Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss.- Learning Shape Representation on Sparse Point Clouds for Volumetric Image Segmentation.- Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation.- 3D U 2 -Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation.- Impact of Adversarial Examples on Deep Learning Segmentation Models.- Multi-Resolution Path CNN with Deep Supervision for Intervertebral Disc Localization and Segmentation.- Automatic paraspinal muscle segmentation in patients with lumbar pathology using deep convolutional neural network.- Constrained Domain Adaptation for Segmentation.- Image Registration.- Image-and-Spatial Transformer Networks for Structure-Guided Image Registration.- Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration.- A deep learning approach to MR-less spatial normalization for tau PET images.- TopAwaRe: Topology-Aware Registration.- Multimodal Data Registration for Brain Structural Association Networks.- Dual-Stream Pyramid Registration Network.- A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration.- Conditional Segmentation in Lieu of Image Registration.- On the applicability of registration un

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Details

  • ISBN-13: 9783030322441
  • ISBN-10: 3030322440
  • Publisher: Springer
  • Publish Date: October 2019
  • Dimensions: 9.21 x 6.14 x 1.81 inches
  • Shipping Weight: 2.77 pounds
  • Page Count: 874

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