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检索条件"任意字段=8th International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 2671 条记录，以下是101-110 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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4th international Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

4th International Workshop on Predictive Intelligence in Med...

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4th international Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030876012

the proceedings contain 25 papers. the special focus in this conference is on Predictive Intelligence in Medicine. the topics include: Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance;self-guided Multi-attention Network for Periventricular Leukomalacia Recognition;opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray;multi-task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer;Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous Graph Attention Convolutional Network;probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution;a Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis Using Ultrasonography;prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Multi-scale Patch Learning with Mammography;the Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification;a Few-Shot Learning Graph Multi-trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint;anatomical Structure-Aware Pulmonary Nodule Detection via Parallel Multi-task RoI Head;towards Cancer Patients Classification Using Liquid Biopsy;foreseeing Survival through ‘Fuzzy Intelligence’: A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion;improving Across Dataset Brain Age Predictions Using Transfer Learning;uncertainty-Based Dynamic Graph Neighborhoods for medical Segmentation;FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates;one Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction;Mixing-AdaSIN: Constructing a De-biased Dataset Using

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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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10th international Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th international Workshop on Clinical image-Based Procedures, CLIP 2020, held in conjunction with the 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

10th International Workshop on Multimodal Learning for Clini...

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10th international Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th international Workshop on Clinical image-Based Procedures, CLIP 2020, held in conjunction with the 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

ISBN: (纸本)9783030609450

the proceedings contain 13 papers. the special focus in this conference is on Multimodal Learning for Clinical Decision Support. the topics include: Towards Automated Diagnosis with Attentive Multi-modal Learning Using Electronic Health Records and Chest X-Rays;LUCAS: LUng CAncer Screening with Multimodal Biomarkers;automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound;Prediction of Type II Diabetes Onset with Computed Tomography and Electronic medical Records;a Radiomics-Based Machine Learning Approach to Assess Collateral Circulation in Ischemic Stroke on Non-contrast Computed Tomography;image-Based Subthalamic Nucleus Segmentation for Deep Brain Surgery with Electrophysiology Aided Refinement;3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research;learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision;single-Shot Deep Volumetric Regression for Mobile medical Augmented Reality;A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge;adversarial Prediction of Radiotherapy Treatment Machine Parameters.

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6th international Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

6th International Workshop and Challenge on Computational Me...

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6th international Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019, held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030397517

the proceedings contain 14 papers. the special focus in this conference is on Computational Methods and Clinical Applications for Spine Imaging. the topics include: A Coarse-to-Fine Deep Heatmap Regression Method for Adolescent Idiopathic Scoliosis Assessment;automated Spinal Curvature Assessment from X-Ray images Using Landmarks Estimation Network via Rotation Proposals;automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks;Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks;preface;vertebral Labelling in Radiographs: Learning a Coordinate Corrector to Enforce Spinal Shape;automatic Cobb Angle Detection Using Vertebra Detector and Vertebra Corners Regression;automatic Spine Curvature Estimation by a Top-Down Approach;seg4Reg Networks for Automated Spinal Curvature Estimation;accurate Automated Keypoint Detections for Spinal Curvature Estimation;Semi-supervised Semantic Segmentation of Multiple Lumbosacral Structures on CT;conditioned Variational Auto-encoder for Detecting Osteoporotic Vertebral Fractures.

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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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11th international Workshop on Machine Learning in medical Imaging, MLMI 2020, held in conjunction with the 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

11th International Workshop on Machine Learning in Medical I...

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11th international Workshop on Machine Learning in medical Imaging, MLMI 2020, held in conjunction with the 23rd international conference on medical image computing and computer assisted intervention, MICCAI 2020

ISBN: (纸本)9783030598600

the proceedings contain 68 papers. the special focus in this conference is on Machine Learning in medical Imaging. the topics include: 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;error Attention Interactive Segmentation of medical image through Matting and Fusion;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;A Novel fMRI Representation Learning Framework with GAN;structural Connectivity Enriched Functional Brain Network U

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1st international Workshop on Advances in Simplifying medical UltraSound, ASMUS 2020, and the 5th international Workshop on Perinatal, Preterm and Paediatric image Analysis, PIPPI 2020, held in conjunction with the 23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

1st International Workshop on Advances in Simplifying Medica...

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1st international Workshop on Advances in Simplifying medical UltraSound, ASMUS 2020, and the 5th international Workshop on Perinatal, Preterm and Paediatric image Analysis, PIPPI 2020, held in conjunction with the 23rd international conference on medical image computing and computer-assisted intervention, MICCAI 2020

ISBN: (纸本)9783030603335

the proceedings contain 33 papers. the special focus in this conference is on Advances in Simplifying medical UltraSound. the topics include: Localizing 2D Ultrasound Probe from Ultrasound image Sequences Using Deep Learning for Volume Reconstruction;augmented Reality-Based Lung Ultrasound Scanning Guidance;multimodality Biomedical image Registration Using Free Point Transformer Networks;label Efficient Localization of Fetal Brain Biometry Planes in Ultrasound through Metric Learning;automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound;unsupervised Cross-domain image Classification by Distance Metric Guided Feature Alignment;dual-Robotic Ultrasound System for In Vivo Prostate Tomography;IoT-Based Remote Control Study of a Robotic Trans-Esophageal Ultrasound Probe via LAN and 5G;differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking;kinematics Data Representations for Skills Assessment in Ultrasound-Guided Needle Insertion;calibrated Bayesian Neural Networks to Estimate Gestational Age and Its Uncertainty on Fetal Brain Ultrasound images;3D Fetal Pose Estimation with Adaptive Variance and Conditional Generative Adversarial Network;atlas-Based Segmentation of the Human Embryo Using Deep Learning with Minimal Supervision;Deformable Slice-to-Volume Registration for Reconstruction of Quantitative T2* Placental and Fetal MRI;a Smartphone-Based System for Real-Time Early Childhood Caries Diagnosis;automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening;Harmonised Segmentation of Neonatal Brain MRI: A Domain Adaptation Approach;a Multi-task Approach Using Positional Information for Ultrasound Placenta Segmentation;spontaneous Preterm Birth Prediction Using Convolutional Neural Networks;Multi-modal Perceptual Adversarial Learning for Longitudinal Prediction of Infant MR images;Efficient Multi-class Fetal Brain Segmentation in High Resolution MRI Reconstructions with Noisy Labels.

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10th international Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

10th International Workshop on Statistical Atlases and Compu...

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10th international Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030390730

the proceedings contain 42 papers. the special focus in this conference is on Statistical Atlases and Computational Models of the Heart. the topics include: 3D Left Ventricular Segmentation from 2D Cardiac MR images Using Spatial Context;towards Hyper-Reduction of Cardiac Models Using Poly-affine Transformations;conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames;learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension;multimodal Cardiac Segmentation Using Disentangled Representation Learning;DeepLA: Automated Segmentation of Left Atrium from interventional 3D Rotational Angiography Using CNN;non-invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-Driven or Model-Based?;deep Learning Surrogate of Computational Fluid Dynamics for thrombus Formation Risk in the Left Atrial Appendage;end-to-end Cardiac Ultrasound Simulation for a Better Understanding of image Quality;Probabilistic Motion Modeling from medical image Sequences: Application to Cardiac Cine-MRI;Manufacturing of Ultrasound- and MRI-Compatible Aortic Valves Using 3D Printing for Analysis and Simulation;deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training;Style Data Augmentation for Robust Segmentation of Multi-modality Cardiac MRI;Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation;An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR image;Cardiac Segmentation of LGE MRI with Noisy Labels;Pseudo-3D Network for Multi-sequence Cardiac MR Segmentation;SK-Unet: An Improved U-Net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR;Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network;Deep Learning Based Multi-modal Cardiac MR image Segmentation;Segmentation of Multimodal Myocardial images Using Shape-Transfer GAN;Automated Multi-sequence Cardiac MRI Segmentation Using S

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8th international Workshop on Machine Learning in medical Imaging, MLMI 2017 held in conjunction with the 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

8th International Workshop on Machine Learning in Medical Im...

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8th international Workshop on Machine Learning in medical Imaging, MLMI 2017 held in conjunction with the 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319673882

the proceedings contain 44 papers. the special focus in this conference is on Machine Learning in medical Imaging. the topics include: From large to small organ segmentation in CT using regional context;motion corruption detection in breast DCE- MRI;detection and localization of drosophila egg chambers in microscopy images;atlas of classifiers for brain MRI segmentation;dictionary learning and sparse coding-based denoising for high-resolution task functional connectivity MRI analysis;multi-factorial age estimation from skeletal and dental MRI volumes;automatic classification of proximal femur fractures based on attention models;joint supervoxel classification forest for weakly-supervised organ segmentation;accurate and consistent hippocampus segmentation through convolutional LSTM and view ensemble;finding dense supervoxel correspondence of cone-beam computed tomography images;multi-scale volumetric convnet with nested residual connections for segmentation of anterior cranial base;3D convolutional neural networks with graph refinement for airway segmentation using incomplete data labels;efficient groupwise registration for brain MRI by fast initialization;sparse multi-view task-centralized learning for ASD diagnosis;inter-subject similarity guided brain network modeling for MCI diagnosis;scalable and fault tolerant platform for distributed learning on private medical data;longitudinally-consistent parcellation of infant population cortical surfaces based on functional connectivity;gradient boosted trees for corrective learning;collage CNN for renal cell carcinoma detection from CT and localizing cardiac structures in fetal heart ultrasound video.

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