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Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV de Dinggang Shen

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV: Lecture Notes in Computer Science, cartea 11767

Editat de Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
en Limba Engleză Paperback – 18 oct 2019
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019.
The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections:
Part I: optical imaging; endoscopy; microscopy.
Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression.
Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging.
Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis.
Part V: computer assisted interventions; MIC meets CAI.
Part VI: computed tomography; X-ray imaging.
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Specificații

ISBN-13: 9783030322502
ISBN-10: 3030322505
Pagini: 809
Ilustrații: XXXVIII, 809 p.
Dimensiuni: 155 x 235 mm
Greutate: 1.17 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Cham, Switzerland

Cuprins

Shape.- A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations.- Exploiting Reliability-guided Aggregation for the Assessment of Curvilinear Structure Tortuosity.- A Surface-theoretic Approach for Statistical Shape Modeling.- Shape Instantiation from A Single 2D Image to 3D Point Cloud with One-stage Learning.- Placental Flattening via Volumetric Parameterization with Dirichlet Energy Regularization.- Fast Polynomial Approximation to Heat Diffusion in Manifolds.- Hierarchical Multi-Geodesic Model for Longitudinal Analysis of Temporal Trajectories of Anatomical Shape and Covariates.- Clustering of longitudinal shape data sets using mixture of separate or branching trajectories.- Group-wise Graph Matching of Cortical Gyral Hinges.- Multi-view Graph Matching of Cortical Landmarks.- Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators.- Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance.- Prediction.- Diagnosis-guided multi-modal feature selection for prognosis prediction of lung squamous cell carcinoma.- Graph convolution based attention model for personalized disease prediction.- Predicting Early Stages of Neurodegenerative Diseases via Multi-task Low-rank Feature Learning.- Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions.- Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.- End-to-End Dementia Status Prediction from Brain MRI using Multi-Task Weakly-Supervised Attention Network.- Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression.- LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke.- Inter-modality Dependence Induced Data Recovery for MCI Conversion Prediction.- Preprocessing, Prediction and Significance: Framework and Application to Brain Imaging.- Early Prediction of Alzheimer's Disease progression using Variational Autoencoder.- Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions.- Detection and Localization.- Uncertainty-informed detection of epileptogenic brain malformations using Bayesian neural networks.- Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network.- Intracranial aneurysms detection in 3D cerebrovascular mesh model with ensemble deep learning.- Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks.- Multiple Landmarks Detection using Multi-Agent Reinforcement Learning.- Spatiotemporal Breast Mass Detection Network (MD-Net) in 4D DCE-MRI Images.- Automated Pulmonary Embolism Detection from CTPA Images using an End-to-End Convolutional Neural Network.- Pixel-wise anomaly ratings using Variational Auto-Encoders.- HR-CAM: Precise Localization of pathology using multi-level learning in CNNs.- Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Diagnosis of MCI Progression.- Automatic Vertebrae Recognition from Arbitrary Spine MRI images by a Hierarchical Self-calibration Detection Framework.- Machine Learning.- Image data validation for medical systems.- Captioning Ultrasound Images Automatically.- Feature Transformers: Privacy Preserving Life Learning Framework for Healthcare Applications.- As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging.- Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification.- Learning task-specific and shared representations in medical imaging.- Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis.- Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.- Fetal Pose Estimation in Volumetric MRI using 3D Convolution Neural Network.- Multi-Stage Prediction Networks for Data Harmonization.- Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube.- Bayesian Volumetric Autoregressive generative models for better semisupervised learning with scarce Medical imaging data.- Data Augmentation for Regression Neural Networks.- A Dirty Multi-task Learning Method for Multi-modal Brain Imaging Genetics.- Robust and Discriminative Brain Genome Association Analysis.- Symmetric Dual Adversarial Connectomic Domain Alignment for Predicting Isomorphic Brain Graph From a Baseline Graph.- Harmonization of Infant Cortical Thickness using Surface-to-Surface Cycle-Consistent Adversarial Networks.- Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference.- Computer-aided Diagnosis.- Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification.- Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis.- Fully Deep Learning for Slit-lamp Photo based NuclearCataract Grading.- Overcoming Data Limitation in Medical Visual Question Answering.- Multi-Instance Multi-Scale CNN for Medical Image Classification.- Improving Uncertainty Estimation in Convolutional Neural Networks Using Inter-rater Agreement.- Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning.- DScGANS: Integrate Domain Knowledge in Training Dual-Path Semi-Supervised Conditional Generative Adversarial Networks and S3VM for Ultrasonography Thyroid Nodules Classification.- Similarity steered generative adversarial network and adaptive transfer learning for malignancy characterization of hepatocellualr carcinoma.- Unsupervised Clustering of Quantitative Imaging Subtypes using Autoencoder and Gaussian Mixture Model.- Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis.- Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics.- Response Estimation through SpatiallyOriented Neural Network and Texture Ensemble (RESONATE).- STructural Rectal Atlas Deformation (StRAD) features for characterizing intra- and peri-wall chemoradiation response on MRI.- Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis.- Deep Multi-modal Latent Representation Learning for Automated Dementia Diagnosis.- Dynamic Spectral Convolution Networks with Assistant Task Training for Early MCI diagnosis.- Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework.- Global and Local Interpretability for Cardiac MRI Classification.- Let's agree to disagree: learning highly debatable multirater labelling.- Coidentifciation of group-level hole structures in brain networks via Hodge Laplacian.- Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers.- Image Reconstruction and Synthesis.- Detection and Correction of Cardiac MRI Motion Artefacts during Reconstruction from k-space.- Exploiting motionfor deep learning reconstruction of extremely-undersampled dynamic MRI.- VS-Net: Variable spitting network for accelerated parallel MRI reconstruction.- A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning.- A Prior Learning Network for Joint Image and Sensitivity Estimation in Parallel MR Imaging.- Consensus Neural Network for Medical Image Denoising with Only Noisy Training Samples.- Consistent Brain Ageing Synthesis.- Hybrid Generative Adversarial Networks for Deep MR to CT Synthesis using Unpaired Data.- Arterial Spin Labeling Images Synthesis via Locally-constrained WGAN-GP Ensemble.- SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis.- Wavelet-Based Semi-Supervised Adversarial Learning for Synthesizing Realistic 7T from 3T MRI.- DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis.