Ophthalmic Medical Image Analysis: 7th International Workshop, Omia 2020, Held in Conjunction with Miccai 2020, Lima, Peru, October 8, 2020, Proceedin, Fu Huazhu, Garvin Mona K., Macgillivray Tom
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: 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 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: 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 proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions.
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.
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 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 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 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: Regular papers.- A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI.- Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view.- Graph convolutional regression of cardiac depolarization from sparse endocardial maps.- A cartesian grid representation of left atrial appendages for deep learning based estimation of thrombogenic risk predictors.- Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks.- Modelling Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions- Towards mesh-free patient-specific mitral valve modeling.- PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps.- Automatic Detection of Landmarks for Fast Cardiac MR Image Registration.- Quality-aware semi-supervised learning for CMR segmentation.- Estimation of imaging biomarker's progression in post-infarct patients using cross-sectional data.- PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data.- Shape constrained CNN for cardiac MR segmentation with simultaneous prediction of shape and pose parameters.- Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI.- Estimation of Cardiac Valve Annuli Motion with Deep Learning.- 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration.- Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning.- M&Ms challenge.- Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation.- Disentangled Representations for Domain-generalized Cardiac Segmentation.- A 2-step Deep Learning method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation.- Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information.- Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer.- Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation.- Studying Robustness of Segmantic Segmentation under Domain Shift in cardiac MRI.- A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences.- Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-Gate UNET.- Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation.- Deidentifying MRI data domain by iterative backpropagation.- A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net.- Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation.- Style-invariant Cardiac Image Segmentation with Test-time Augmentation.- EMIDEC challenge.- Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI.- Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI.- Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information.- SM2N2: A Stacked Architecture for Multimodal Data and its Application to Myocardial Infarction Detection.- A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI.- Efficient 3D deep learning for myocardial diseases segmentation.- Deep-learning-based myocardial pathology detection.- Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks.- Uncertainty-based Segmentation of Myocardial Infarction Areas on Cardiac MR images.- Anatomy Prior Based U-net for Pathology Segmentation wi
Описание: Brain Tumor Segmentation.- Lightweight U-Nets for Brain Tumor Segmentation.- Efficient Brain Tumour Segmentation using Co-registered Data and Ensembles of Specialised Learners.- Efficient MRI Brain Tumor Segmentation using Multi-Resolution Encoder-Decoder Networks.- Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework.- HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation.- H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task.- 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma Segmentation.- Attention U-Net with Dimension-hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image Segmentation.- MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation.- Glioma Segmentation with 3D U-Net Backed with Energy- Based Post- Processing.- nnU-Net for Brain Tumor Segmentation.- A Deep Random Forest Approach for Multimodal Brain Tumor Segmentation.- Brain tumor segmentation and associated uncertainty evaluation using Multi-sequences MRI Mixture Data Preprocessing.- A Deep supervision CNN network for Brain tumor Segmentation.- Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans.- Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation.- Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fusion.- Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge.- 3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction.- Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI using Selective Kernel Networks.- 3D brain tumor segmentation and survival prediction using ensembles of Convolutional Neural Networks.- Brain Tumour Segmentation using Probabilistic U-Net.- Segmenting Brain Tumors from MRI Using Cascaded 3D U-Nets.- A Deep Supervised U-Attention Net for Pixel-wise Brain Tumor Segmentation.- A two stage atrous convolution neural network for brain tumor segmentation.- TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI data.- Brain Tumor Segmentation and Survival Prediction using Automatic Hardmining in 3D CNN Architecture.- Some New Tricks for Deep Glioma Segmentation.- PieceNet: A Redundant UNet Ensemble.- Cerberus: A Multi-headed Network for BrainTumor Segmentation.- An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients based on Volumetric and Shape Features.- Squeeze-and-Excitation Normalization for Brain Tumor Segmentation.- Modified MobileNet for Patient Survival Prediction.- Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation.- Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified U-Net.- DR-Unet104 for Multimodal MRI brain tumor segmentation.- Glioma Sub-region Segmentation on Multi-parameter MRI with Label Dropout.- Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation.- Learning Dynamic Convolutions for Multi-Modal 3D MRI Brain Tumor Segmentation.- Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification.- Automatic Glioma Grading Based on Two-stage Networks by Integrating Pathology and MRI Images.- Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology Images.- Multimodal brain tumor classification.- A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSI.- CNN-based Fully Automatic Glioma Classification with Multi-modal Medical Images.- Glioma Classification Using Multimodal Radiology and Histology Data.
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