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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是11-20 订阅

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3rd international Workshop on Uncertainty for Safe Utilization of Machine Learning in medical Imaging, UNSURE 2021, and the 6th international Workshop on Perinatal, Preterm and Paediatric image Analysis, PIPPI 2021, held in conjunction with the 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

3rd International Workshop on Uncertainty for Safe Utilizati...

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3rd international Workshop on Uncertainty for Safe Utilization of Machine Learning in medical Imaging, UNSURE 2021, and the 6th international Workshop on Perinatal, Preterm and Paediatric image Analysis, PIPPI 2021, held in conjunction with the 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030877347

The proceedings contain 27 papers. The special focus in this conference is on Uncertainty for Safe Utilization of Machine Learning in medical Imaging. The topics include: Robust Selective Classification of Skin Lesions with Asymmetric Costs;confidence-Based Out-of-Distribution Detection: A Comparative Study and Analysis;novel Disease Detection Using Ensembles with Regularized Disagreement;automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture;Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction;Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI;myelination of Preterm Brain Networks at Adolescence;A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI;Segmentation of the Cortical Plate in Fetal Brain MRI with a Topological Loss;accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic;Fetal Brain MRI Measurements Using a Deep Learning Landmark Network with Reliability Estimation;CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI;Detection of Injury and Automated Triage of Preterm Neonatal MRI Using Patch-Based Gaussian Processes;assessment of Regional Cortical Development Through Fissure Based Gestational Age Estimation in 3D Fetal Ultrasound;texture-Based Analysis of Fetal Organs in Fetal Growth Restriction;Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI;analysis of the Anatomical Variability of Fetal Brains with Corpus Callosum Agenesis;predicting Preterm Birth Using Multimodal Fetal Imaging;Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR;improving the Reliability of Semantic Segmentation of medical images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning.

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3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

3rd MICCAI Workshop on Domain Adaptation and Representation ...

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3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030877217

The proceedings contain 23 papers. The special focus in this conference is on Domain Adaptation and Representation Transfer. The topics include: Learning from Partially Overlapping Labels: image Segmentation Under Annotation Shift;unsupervised Domain Adaption via Similarity-Based Prototypes for Cross-Modality Segmentation;classification and Generation of Microscopy images with Plasmodium Falciparum via Artificial Neural Networks Using Low Cost Settings;Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN;Low-Dose Dynamic CT Perfusion Denoising Without Training Data;recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory;COVID-Net US: A Tailored, Highly Efficient, Self-attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-Care Ultrasound Imaging;inter-domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning;self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning;sickle Cell Disease Severity Prediction from Percoll Gradient images Using Graph Convolutional Networks;continual Domain Incremental Learning for Chest X-Ray Classification in Low-Resource Clinical Settings;deep Learning Based Automatic Detection of Adequately Positioned Mammograms;can Non-specialists Provide High Quality Gold Standard Labels in Challenging Modalities?;FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation;adversarial Continual Learning for Multi-domain Hippocampal Segmentation;self-supervised Multimodal Generalized Zero Shot Learning for Gleason Grading;self-supervised Learning of Inter-label Geometric Relationships for Gleason Grade Segmentation;stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training;transductive image Segmentation: Self-training and Effect of Uncertainty Estimation;Unsupervised Domain Adaptation with Semantic Consistency Across Heterogeneous Modalities for MR

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2nd international Workshop on Uncertainty for Safe Utilization of Machine Learning in medical Imaging, UNSURE 2020, and the 3rd international Workshop on Graphs in Biomedical image Analysis, GRAIL 2020, held in conjunction with the 23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

2nd International Workshop on Uncertainty for Safe Utilizati...

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2nd international Workshop on Uncertainty for Safe Utilization of Machine Learning in medical Imaging, UNSURE 2020, and the 3rd international Workshop on Graphs in Biomedical image Analysis, GRAIL 2020, held in conjunction with the 23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

ISBN: (纸本)9783030603649

The proceedings contain 20 papers. The special focus in this conference is on Uncertainty for Safe Utilization of Machine Learning in medical Imaging. The topics include: Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps;weight Averaging Impact on the Uncertainty of Retinal Artery-Venous Segmentation;improving Pathological Distribution Measurements with Bayesian Uncertainty;improving Reliability of Clinical Models Using Prediction Calibration;uncertainty Estimation in medical image Denoising with Bayesian Deep image Prior;Preface UNSURE 2020;preface grail 2020;image Registration via Stochastic Gradient Markov Chain Monte Carlo;Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability;clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates;Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences;graph Matching Based Connectomic Biomarker with Learning for Brain Disorders;multi-scale Profiling of Brain Multigraphs by Eigen-Based Cross-diffusion and Heat Tracing for Brain State Profiling;graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation;min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth;geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface;the GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for medical image Classification;Intraoperative Liver Surface Completion with Graph Convolutional VAE;RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in medical image Segmentation.

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3rd international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2020, the 2nd international Workshop on medical image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th international Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

3rd International Workshop on Interpretability of Machine In...

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3rd international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2020, the 2nd international Workshop on medical image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th international Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

ISBN: (纸本)9783030611651

The proceedings contain 29 papers. The special focus in this conference is on Interpretability of Machine Intelligence in medical image computing. The topics include: Reliable Saliency Maps for Weakly-Supervised Localization of Disease Patterns;Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound images;recovering the Imperfect: Cell Segmentation in the Presence of Dynamically Localized Proteins;imimic 2020 preface;Assessing Attribution Maps for Explaining CNN-Based Vertebral Fracture Classifiers;semi-supervised Instance Segmentation with a Learned Shape Prior;COMe-SEE: Cross-modality Semantic Embedding Ensemble for Generalized Zero-Shot Diagnosis of Chest Radiographs;semi-supervised Machine Learning with MixMatch and Equivalence Classes;Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CT;uncertainty Estimation in medical image Localization: Towards Robust Anterior Thalamus Targeting for Deep Brain Stimulation;a Case Study of Transfer ofLesion-Knowledge;transfer Learning with Joint Optimization for Label-Efficient medical image Anomaly Detection;unsupervised Wasserstein Distance Guided Domain Adaptation for 3D Multi-domain Liver Segmentation;hydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification;semi-supervised Classification of Chest Radiographs;projective Latent interventions for Understanding and Fine-Tuning Classifiers;risk of Training Diagnostic Algorithms on Data with Demographic Bias;semi-weakly Supervised Learning for Prostate Cancer image Classification with Teacher-Student Deep Convolutional Networks;Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels;easierPath: An Open-Source Tool for Human-in-the-Loop Deep Learning of Renal Pathology;imbalance-Effective Active Learning in Nucleus, Lymphocyte and Plasma Cell Detection;Labeling of Multilingual Breast MRI R

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3rd international Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

3rd International Workshop on Connectomics in NeuroImaging, ...

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3rd international Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030323905

The proceedings contain 13 papers. The special focus in this conference is on international Workshop on Connectomics in NeuroImaging, held in conjunction with the international conference on medical image computing and computer assisted intervention. The topics include: Constraining disease progression models using subject specific connectivity priors;hemodynamic matrix factorization for functional magnetic resonance imaging;network dependency index stratified subnetwork analysis of functional connectomes: An application to autism;A novel graph neural network to localize eloquent cortex in brain tumor patients from resting-state FMRI connectivity;graph morphology-based genetic algorithm for classifying late dementia states;covariance shrinkage for dynamic functional connectivity;rapid acceleration of the permutation test via transpositions;heat kernels with functional connectomes reveal atypical energy transport in peripheral subnetworks in autism;a mass multivariate edge-wise approach for combining multiple connectomes to improve the detection of group differences;adversarial connectome embedding for mild cognitive impairment identification using cortical morphological networks;a machine learning framework for accurate functional connectome fingerprinting and an application of a siamese network.

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1st Cerebral Aneurysm Detection and Analysis challenge, CADA 2020 held in Conjunction with 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

1st Cerebral Aneurysm Detection and Analysis challenge, CADA...

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1st Cerebral Aneurysm Detection and Analysis challenge, CADA 2020 held in Conjunction with 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

ISBN: (纸本)9783030728618

The proceedings contain 11 papers. The special focus in this conference is on medical image computing and computer assisted intervention. The topics include: Intracranial Aneurysm Rupture Prediction with Computational Fluid Dynamics Point Clouds;CADA: Clinical Background and Motivation;deep Learning-Based 3D U-Net Cerebral Aneurysm Detection;Detect and Identify Aneurysms Based on Adjusted 3D Attention UNet;a ν -Net: Automatic Detection and Segmentation of Aneurysm;3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation Challenge;exploring Large Context for Cerebral Aneurysm Segmentation;CADA Challenge: Rupture Risk Assessment Using Computational Fluid Dynamics;Cerebral Aneurysm Rupture Risk Estimation Using XGBoost and Fully Connected Neural Network.

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6th international MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd medical image computing for computer assisted intervention conference, MICCAI 2020

6th International MICCAI Brainlesion Workshop, BrainLes 2020...

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6th international MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd medical image computing for computer assisted intervention conference, MICCAI 2020

ISBN: (纸本)9783030720834

The proceedings contain 91 papers. The special focus in this conference is on medical image computing for computer assisted intervention. The topics include: Preface;glioma Diagnosis and Classification: Illuminating the Gold Standard;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;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 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;Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods;brain Tumor Segmentation Network Using Attention-Based Fusion and Spatial Relationship Constraint;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;Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics.

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6th international MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd medical image computing for computer assisted intervention conference, MICCAI 2020

6th International MICCAI Brainlesion Workshop, BrainLes 2020...

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6th international MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd medical image computing for computer assisted intervention conference, MICCAI 2020

ISBN: (纸本)9783030720865

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Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound images Challenge, TN-SCUI 2020 held in conjunction with 23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

Anatomical Brain Barriers to Cancer Spread: Segmentation fro...

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Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound images Challenge, TN-SCUI 2020 held in conjunction with 23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

ISBN: (纸本)9783030718268

The proceedings contain 19 papers. The special focus in this conference is on medical image computing and computer-assisted intervention. The topics include: Cross-Modality Brain Structures image Segmentation for the Radiotherapy Target Definition and Plan Optimization;learning a Deformable Registration Pyramid;deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels;multi-step, Learning-Based, Semi-supervised image Registration Algorithm;Using Elastix to Register Inhale/Exhale Intrasubject Thorax CT: A Unsupervised Baseline to the Task 2 of the Learn2Reg Challenge;automatic Segmentation and Classification of Thyroid Nodules in Ultrasound images with Convolutional Neural Networks;LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound images;coarse to Fine Ensemble Network for Thyroid Nodule Segmentation;Cascade UNet and CH-UNet for Thyroid Nodule Segmentation and Benign and Malignant Classification;identifying Thyroid Nodules in Ultrasound images Through Segmentation-Guided Discriminative Localization;cascaded Networks for Thyroid Nodule Diagnosis from Ultrasound images;domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread;ensembled ResUnet for Anatomical Brain Barriers Segmentation;an Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume;automatic Segmentation of Brain Structures for Treatment Planning Optimization and Target Volume Definition;a Bi-directional, Multi-modality Framework for Segmentation of Brain Structures;large Deformation image Registration with Anatomy-Aware Laplacian Pyramid Networks;discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge;Variable Fraunhofer MEVIS RegLib Comprehensively Applied to Learn2Reg Challenge.

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3rd international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2018 Held in Conjunction with 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

3rd International Workshop on Simulation and Synthesis in Me...

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3rd international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2018 Held in Conjunction with 21st international conference on medical image computing and computer-assisted intervention, MICCAI 2018

ISBN: (纸本)9783030005351

The proceedings contain 14 papers. The special focus in this conference is on Simulation and Synthesis in medical Imaging. The topics include: Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images;lung nodule synthesis using cnn-based latent data representation;RS-net: Regression-segmentation 3d cnn for synthesis of full resolution missing brain mri in the presence of tumours;generating magnetic resonance spectroscopy imaging data of brain tumours from linear, non-linear and deep learning models;Data augmentation using synthetic lesions improves machine learning detection of microbleeds from MRI;deep harmonization of inconsistent mr data for consistent volume segmentation;cross-modality image synthesis from unpaired data using cyclegan: Effects of gradient consistency loss and training data size;a machine learning approach to diffusion mri partial volume estimation;unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks;deep boosted regression for mr to ct synthesis;model-based generation of synthetic 3d time-lapse sequences of multiple mutually interacting motile cells with filopodia;MRI to FDG-PET: Cross-modal synthesis using 3d u-net for multi-modal alzheimer’s classification.

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