Thoracic Image Analysis: Second International Workshop, Tia 2020, Held in Conjunction with Miccai 2020, Lima, Peru, October 8, 2020, Proceeding, Petersen Jens, San Josй Estйpar Raъl, Schmidt-Richberg Alexander
Описание: This book constitutes the proceedings of the International Workshop on Shape in Medical Imaging, ShapeMI 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer Assistend Intervention, MICCAI 2020, in October 2020.
Описание: This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.
Описание: This book constitutes refereed proceedings of the Second International Workshop on Deep Learning for Human Activity Recognition, DL-HAR 2020, held in conjunction with IJCAI-PRICAI 2020, in Kyoto, Japan, in January 2021.
Описание: This book constitutes the refereed proceedings of the Second International Workshop on Human Brain and Artificial Intelligence, HBAI 2020, held in conjunction with IJCAI-PRICAI 2020, Kyoto, Japan, in January 2021.
Описание: Invited Papers.- Glioma Diagnosis and Classification: Illuminating the Gold Standard.- Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods.- Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics.- Brain Lesion Image Analysis.- Automatic Segmentation of Non-Tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks.- Convolutional neural network with asymmetric encoding and decoding structure for brain vessel segmentation on computed tomographic angiography.- Volume Preserving Brain Lesion Segmentation.- Microstructural modulations in the hippocampus allow to characterizing relapsing-remitting versus primary progressive multiple sclerosis.- Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology.- Multivariate analysis is sufficient for lesion-behaviour mapping.- Label-Efficient Multi-Task Segmentation using Contrastive Learning.- Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation.- MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection.- Unsupervised 3D Brain Anomaly Detection.- Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI Tejas Sudharshan Mathai, Yi Wang, Nathan Cross.- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.- Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions.- Brain Tumor Segmentation.- Brain Tumor Segmentation Using Dual-Path Attention U-net in 3D MRI Images.- Multimodal Brain Image Analysis and Survival Prediction.- Using Neuromorphic Attention-based Neural Networks.- Context Aware 3D UNet for Brain Tumor Segmentation.- Modality-Pairing Learning for Brain Tumor Segmentation.- Transfer Learning for Brain Tumor Segmentation.- Efficient embedding network for 3D brain tumor segmentation.- Segmentation of the multimodal brain tumor images used Res-U-Net.- Vox2Vox: 3D-GAN for Brain Tumour Segmentation.- Automatic Brain Tumor Segmentation with Scale Attention Network.- Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction.- Overall Survival Prediction for Glioblastoma on Pre-Treatment MRI Using Robust Radiomics and Priors.- Glioma segmentation using encoder-decoder network and survival prediction based on cox analysis.- Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution.- Brain tumour segmentation using a triplanar ensemble of U-Nets on MR images.- MRI brain tumor segmentation using a 2D-3D U-Net ensemble.- Multimodal Brain Tumor Segmentation and Survival Prediction Using a 3D Self-Ensemble ResUNet.- MRI Brain Tumor Segmentation and Uncertainty Estimation using 3D-UNet architectures.- Utility of Brain Parcellation in Enhancing Brain Tumor Segmentation and Survival Prediction.- Uncertainty-driven refinement of tumor core segmentation using 3D-to-2D networks with label uncertainty.- Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation.- MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking.- A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation.- Ensemble of Two Dimensional Networks for Bain Tumor Segmentation.- Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation.- Low-Rank Convolutional Networks for Brain Tumor Segmentation.- Brain tumour segmentation using cascaded 3D densely-connected U-net.- Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival Prediction.- Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network.- Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided S
Описание: This book constitutes the refereed proceedings of the 6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.
Описание: This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.
Описание: This book constitutes the proceedings of the Third International Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.
Описание: This book constitutes the refereed proceedings of the 5th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The 19 full papers presented were carefully reviewed and selected from 27 submissions.
Описание: Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder with Resting-State fMRI.- Error Attention Interactive Segmentation of Medical Images through Matting and Fusion.- A Novel fMRI Representation Learning Framework with GAN.- Semi-supervised Segmentation with Self-Training Based on Quality Estimation and Refinement.- 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies.- Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network.- Self-Recursive Contextual Network for Unsupervised 3D Medical Image Registration.- Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy.- Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows.- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest.- A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation.- Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network.- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior.- Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation.- GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes.- Anatomy-Aware Cardiac Motion Estimation.- Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation.- LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI.- Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation.- Boundary-aware Network for Kidney Tumor Segmentation.- O-Net: An Overall Convolutional Network for Segmentation Tasks.- Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints.- EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis.- Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation.- Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer.- Exploring Functional Difference between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks.- Detection of Ischemic Infarct Core in Non-Contrast Computed Tomography.- Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers.- Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients.- Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet.- Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification.- Multi-tasking Siamese Networks for Breast Mass Detection using Dual-view Mammogram Matching.- 3D Volume Reconstruction from Single Lateral X-ray Image via Cross-Modal Discrete Embedding Transition.- Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks.- A Deep Network for Joint Registration and Reconstruction of Images with Pathologies.- Learning Conditional Deformable Shape Templates for Brain Anatomy .- Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.- Unsupervised Learning for Spherical Surface Registration.- Anatomy-guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI.- Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization.- Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors.- Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening .- Importance Driven Continual Learning for Segmentation Across Domains.- RDCNet: Instance segmentation with a minimalist recurrent residual network.- Automatic Segmentation of Achilles Tend
Описание: This book constitutes the First Automatization of Cranial Implant Design in Cranioplasty Challenge, AutoImplant 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.
Описание: This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions.
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