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检索条件"任意字段=6th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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12th international Workshop on Machine Learning in medical Imaging, MLMI 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

12th International Workshop on Machine Learning in Medical I...

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12th international Workshop on Machine Learning in medical Imaging, MLMI 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030875886

the proceedings contain 71 papers. the special focus in this conference is on Machine Learning in medical Imaging. the topics include: Learning Structure from Visual Semantic Features and Radiology Ontology for Lymph Node Classification on MRI;improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment;cell Counting by a Location-Aware Network;exploring Gyro-Sulcal Functional Connectivity Differences Across Task Domains via Anatomy-Guided Spatio-Temporal Graph Convolutional Networks;stairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis;Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-Ray images;transfer Learning with a Layer Dependent Regularization for medical image Segmentation;Multi-scale Self-supervised Learning for Multi-site Pediatric Brain MR image Segmentation with Motion/Gibbs Artifacts;deep Active Learning for Dual-View Mammogram Analysis;Learning Transferable 3D-CNN for MRI-Based Brain Disorder Classification from Scratch: An Empirical Study;statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound;semi-supervised Learning Regularized by Adversarial Perturbation and Diversity Maximization;transforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes;A Recurrent Two-Stage Anatomy-Guided Network for Registration of Liver DCE-MRI;learning Infant Brain Developmental Connectivity for Cognitive Score Prediction;hierarchical 3D Feature Learning forPancreas Segmentation;voxel-Wise Cross-Volume Representation Learning for 3D Neuron Reconstruction;Diagnosis of Hippocampal Sclerosis from Clinical Routine Head MR images Using Structure-constrained Super-Resolution Network;u-Net Transformer: Self and Cross Attention for medical image Segmentation;pre-biopsy Multi-class Classification of Breast Lesion Pathology in Mammograms;knee Cartilages Segmentation Based on Multi-scale Cascaded Neural Networks;co-segmentation

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10th international Workshop on Clinical image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

10th International Workshop on Clinical Image-Based Procedur...

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10th international Workshop on Clinical image-Based Procedures, CLIP 2021, 2nd MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, 1st MICCAI Workshop, LL-COVID19, 1st Secure and Privacy-Preserving Machine Learning for medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030908737

the proceedings contain 17 papers. the special focus in this conference is on Clinical image-Based Procedures. the topics include: DCL preface;LL-COVID-19 preface;PPML preface;intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction;multi-task Federated Learning for Heterogeneous Pancreas Segmentation;federated Learning in the Cloud for Analysis of medical images - Experience with Open Source Frameworks;on the Fairness of Swarm Learning in Skin Lesion Classification;Lessons Learned from the Development and Application of medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next;the Role of Pleura and Adipose in Lung Ultrasound AI;DuCN: Dual-Children Network for medical Diagnosis and Similar Case Recommendation Towards COVID-19;data Imputation and Reconstruction of Distributed Parkinson’s Disease Clinical Assessments: A Comparative Evaluation of Two Aggregation Algorithms;defending medical image Diagnostics Against Privacy Attacks Using Generative Methods: Application to Retinal Diagnostics;generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning;Bayesian Graph Neural Networks for EEG-Based Emotion Recognition;ViTBIS: Vision Transformer for Biomedical image Segmentation;Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes;development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning;learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback.

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2nd international Workshop on Advances in Simplifying medical UltraSound, ASMUS 2021 held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

2nd International Workshop on Advances in Simplifying Medica...

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2nd international Workshop on Advances in Simplifying medical UltraSound, ASMUS 2021 held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

ISBN: (纸本)9783030875824

the proceedings contain 22 papers. the special focus in this conference is on Advances in Simplifying medical UltraSound. the topics include: Evaluation of Low-Cost Hardware Alternatives for 3D Freehand Ultrasound Reconstruction in image-Guided Neurosurgery;Application Potential of Robot-Guided Ultrasound During CT-Guided interventions;towards Scale and Position Invariant Task Classification Using Normalised Visual Scanpaths in Clinical Fetal Ultrasound;efficient Echocardiogram View Classification with Sampling-Free Uncertainty Estimation;contrastive Learning for View Classification of Echocardiograms;Imaging Biomarker Knowledge Transfer for Attention-Based Diagnosis of COVID-19 in Lung Ultrasound Videos;endoscopic Ultrasound image Synthesis Using a Cycle-Consistent Adversarial Network;realistic Ultrasound image Synthesis for Improved Classification of Liver Disease;adaptable image Quality Assessment Using Meta-Reinforcement Learning of Task Amenability;multimodal Continual Learning with Sonographer Eye-Tracking in Fetal Ultrasound;deep Video Networks for Automatic Assessment of Aortic Stenosis in Echocardiography;pruning MobileNetV2 for Efficient Implementation of Minimum Variance Beamforming;automatic Fetal Gestational Age Estimation from First Trimester Scans;development and Evaluation of Intraoperative Ultrasound Segmentation with Negative image Frames and Multiple Observer Labels;automatic Tomographic Ultrasound Imaging Sequence Extraction of the Anal Sphincter;Lung Ultrasound Segmentation and Adaptation Between COVID-19 and Community-Acquired Pneumonia;an Efficient Tracker for thyroid Nodule Detection and Tracking During Ultrasound Screening;transBridge: A Lightweight Transformer for Left Ventricle Segmentation in Echocardiography;Adversarial Affine Registration for Real-Time Intraoperative Registration of 3-D US-US for Brain Shift Correction;robust Ultrasound-to-Ultrasound Registration for Intra-operative Brain Shift Correction with a Siamese Neural Network.

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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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4th international Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th international Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with medical image computing and computer assisted intervention, MICCAI 2024

4th International Workshop on Imaging and Treatment Challeng...

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4th international Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th international Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031811005

the proceedings contain 12 papers. the special focus in this conference is on Imaging and Treatment Challenges and international Challenge on Ischemic Stroke Lesion Segmentation Challenge, Held in Conjunction with medical image computing and computer assisted intervention. the topics include: Bayesian Uncertainty Estimation Improves nnU-Net Generalization to Unseen Sites for Stroke Lesion Segmentation;Usefulness of Monoenergetic Non-contrast CT and X-Map images for Deep Learning-Based Stroke Lesion Segmentation;improved Stroke Lesion Segmentation via Cross-Model Knowledge Distillation;Virtual DSA for Learning Contrast Agent Dynamics in Projection Space;robust Feature Selection for Classifying Early Ischemic Changes in Posterior Stroke;ArterialGNet: Impossible Femoral Access Prediction in Stroke Mechanical thrombectomy with Vascular Centerline Graph Embeddings;Spatio-Temporal Deep Learning for Final Infarct Prediction Using Acute Stroke CT Perfusion Data;Utilizing Baseline Infarct and Penumbra Masks to Improve NCCT and CTA Based Final Infarct Prediction;final Stroke Infarct Segmentation Using Deep Neural Networks;A Multi-modal Deep Learning Framework for Final Infarct Prediction in Acute Ischemic Stroke: Combining CTA, NCCT, and Clinical Data.

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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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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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