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{ "item_title" : "Medical Image Computing and Computer Assisted Intervention - Miccai 2019", "item_author" : [" Dinggang Shen", "Tianming Liu", "Terry M. Peters "], "item_description" : "Neuroimage Reconstruction and Synthesis.- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior.- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging.- Model Learning: Primal Dual Networks for Fast MR imaging.- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging.- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework.- Deep Learning Based Framework for Direct Reconstruction of PET Images.- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction.- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization.- Single Image Based Reconstruction of High Field-like MR Images.- Deep Neural Network for QSM Background Field Removal.- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting.- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting.- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions.- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks.- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control.- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages.- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map.- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading.- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression.- Neuroimage Segmentation.- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation.- 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI.- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants.- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation.- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning.- Scalable Neural Architecture Search for 3D Medical Image Segmentation.- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images.- High Resolution Medical Image Segmentation using Data-swapping Method.- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies.- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network.- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke.- Brain Segmentation from k-space with End-to-end Recurrent Attention Network.- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images.- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion.- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation.- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets.- Generative adversarial network for segmentation of motion affected neonatal brain MRI.- Interactive deep editing framework for medical image segmentation.- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices.- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation.- Unsupervised deep learning for Bayesian brain MRI segmentation.- Online atlasing using an iterative centroid.- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation.- Complete Fetal Head Compounding from Multi-View 3D Ultrasound.- SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization f", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/3/03/032/247/3030322475_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
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

Neuroimage Reconstruction and Synthesis.- Isotropic MRI Super-Resolution Reconstruction with Multi-Scale Gradient Field Prior.- A Two-Stage Multi-Loss Super-Resolution Network For Arterial Spin Labeling Magnetic Resonance Imaging.- Model Learning: Primal Dual Networks for Fast MR imaging.- Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging.- Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework.- Deep Learning Based Framework for Direct Reconstruction of PET Images.- Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction.- Reconstruction of Isotropic High-Resolution MR Image from Multiple Anisotropic Scans using Sparse Fidelity Loss and Adversarial Regularization.- Single Image Based Reconstruction of High Field-like MR Images.- Deep Neural Network for QSM Background Field Removal.- RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting.- RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting.- GANReDL: Medical Image enhancement using a generative adversarial network with real-order derivative induced loss functions.- Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks.- Semi-Supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control.- Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-Modal Neuroimages.- Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map.- CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading.- Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression.- Neuroimage Segmentation.- Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation.- 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI.- Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants.- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation.- Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning.- Scalable Neural Architecture Search for 3D Medical Image Segmentation.- Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images.- High Resolution Medical Image Segmentation using Data-swapping Method.- X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies.- Multi-View Semi-supervised 3D Whole Brain Segmentation with a Self-Ensemble Network.- CLCI-Net: Cross-Level Fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke.- Brain Segmentation from k-space with End-to-end Recurrent Attention Network.- Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images.- CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion.- A Joint 3D+2D Fully Convolutional Framework for Subcortical Segmentation.- U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets.- Generative adversarial network for segmentation of motion affected neonatal brain MRI.- Interactive deep editing framework for medical image segmentation.- Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices.- Improving Multi-Atlas Segmentation by Convolutional Neural Network Based Patch Error Estimation.- Unsupervised deep learning for Bayesian brain MRI segmentation.- Online atlasing using an iterative centroid.- ARS-Net: Adaptively Rectified Supervision Network for Automated 3D Ultrasound Image Segmentation.- Complete Fetal Head Compounding from Multi-View 3D Ultrasound.- SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization f

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Details

  • ISBN-13: 9783030322472
  • ISBN-10: 3030322475
  • Publisher: Springer
  • Publish Date: October 2019
  • Dimensions: 9.21 x 6.14 x 1.83 inches
  • Shipping Weight: 2.8 pounds
  • Page Count: 888

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