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

共 3007 条记录，以下是81-90 订阅

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3rd MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the 2nd MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

3rd MICCAI Workshop on Distributed, Collaborative, and Feder...

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3rd MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the 2nd MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

the proceedings contain 18 papers. the special focus in this conference is on Distributed, Collaborative, and Federated Learning. the topics include: DeMed: A Novel and60;Efficient Decentralized Learning Framework ...

ISBN: (纸本)9783031185229

the proceedings contain 18 papers. the special focus in this conference is on Distributed, Collaborative, and Federated Learning. the topics include: DeMed: A Novel and Efficient Decentralized Learning Framework for medical images Classification on Blockchain;cluster Based Secure Multi-party Computation in Federated Learning for Histopathology images;towards More Efficient Data Valuation in Healthcare Federated Learning Using Ensembling;towards Real-World Federated Learning in medical image Analysis Using Kaapana;towards Sparsified Federated Neuroimaging Models via Weight Pruning;Enhancing Portable OCT image Quality via GANs for AI-Based Eye Disease Detection;deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions;verifiable and Energy Efficient medical image Analysis with Quantised Self-attentive Deep Neural Networks;LRH-Net: A Multi-level Knowledge Distillation Approach for Low-Resource Heart Network;FedAP: Adaptive Personalization in Federated Learning for Non-IID Data;data Stealing Attack on medical images: Is It Safe to Export Networks from Data Lakes?;can Collaborative Learning Be Private, Robust and Scalable?;split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation;joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning;GAN Latent Space Manipulation and Aggregation for Federated Learning in medical Imaging;A Specificity-Preserving Generative Model for Federated MRI Translation;content-Aware Differential Privacy with Conditional Invertible Neural Networks.

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1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, Held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for D...

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1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, Held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

the proceedings contain 17 papers. the special focus in this conference is on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy. the topics include: Lightweight and60;Accurate: ShuffleNet for60;Diabetic Re...

ISBN: (纸本)9783031893872

the proceedings contain 17 papers. the special focus in this conference is on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy. the topics include: Lightweight and Accurate: ShuffleNet for Diabetic Retinopathy and EfficientNet for Diabetic Macular Edema Diagnosis;efficient Deep Learning Models for Ultra-widefield Fundus Imaging for Diabetic Retinopathy;bag of Tricks for Ultra-widefield Fundus image Quality Assessment;bag of Tricks for Diabetic Retinopathy and Diabetic Macular Edema Classification in Ultra-widefield Imaging;deep Self-supervised Learning for Ultra-widefield Fundus image Quality Assessment;Reliable DL-Based Referable Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ultra-widefield Fundus images;deep Learning-Based Detection of Referable Diabetic Retinopathy and Macular Edema Using Ultra-widefield Fundus Imaging;a Comprehensive Approach to Diabetic Retinopathy Classification: Combining ResNet34 with Enhanced Preprocessing for Ultra-widefield Fundus Imaging;an Ultra-efficient Method for Real-Time Ultra-widefield Fundus image Quality Assessment;ultra-fast Detection of Referable Diabetic Retinopathy and Macular Edema in Ultra-widefield Fundus Imaging Using a Unified Risk Score;efficient Deep Learning Approaches for Processing Ultra-widefield Retinal Imaging;efficientNet-B1 Based Diabetic Retinopathy Detection from Ultra-widefield Fundus images;many-MobileNet: Multi-model Augmentation for Robust Retinal Disease Classification;DME-MobileNet: Fine-Tuning nnMobileNet for Diabetic Macular Edema Classification;automatic Identification Method for Diabetic Macular Edema in Ultra-widefield Fundus images.

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2nd international challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

2nd International challenge on Kidney and Kidney Tumor Segme...

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2nd international challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

ISBN: (纸本)9783030983840

the proceedings contain 21 papers. the special focus in this conference is on Kidney and Kidney Tumor Segmentation. the topics include: 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT;A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans;A Cascaded 3D Segmentation Model for Renal Enhanced CT images;Contrast-Enhanced CT Renal Tumor Segmentation;An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans;Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge;a Two-Stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation;Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT images;automated Kidney Tumor Segmentation with Convolution and Transformer Network;a Coarse-to-Fine Framework for the 2021 Kidney and Kidney Tumor Segmentation Challenge;three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net;2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst;Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT;mixup Augmentation for Kidney and Kidney Tumor Segmentation;preface;kidney and Kidney Tumor Segmentation Using a Two-Stage Cascade Framework;less is More: Contrast Attention assisted U-Net for Kidney, Tumor and Cyst Segmentations;automated Machine Learning Algorithm for Kidney, Kidney Tumor, Kidney Cyst Segmentation in Computed Tomography Scans;Modified nnU-Net for the MICCAI KiTS21 Challenge.

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

1st Challenge on Head and Neck Tumor Segmentation for MRI-Gu...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

ISBN: (纸本)9783031832734

the proceedings contain 22 papers. the special focus in this conference is on Head and Neck Tumor Segmentation for MRI-Guided Applications. the topics include: Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation therapy;Head and Neck Tumor Segmentation for MRI-Guided Radiation therapy Using Pre-trained STU-Net Models;Head and Neck Tumor Segmentation of MRI from Pre-and Mid-Radiotherapy with Pre-Training, Data Augmentation and Dual Flow UNet;Head and Neck Tumor Segmentation on MRIs with Fast and Resource-Efficient Staged nnU-Nets;Deep Learning for Longitudinal Gross Tumor Volume Segmentation in MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer;Enhancing Head and Neck Tumor Segmentation in MRI: the Impact of image Preprocessing and Model Ensembling;UMamba Adjustment: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and NnU-Net ResEnc Planner;Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-Guided Radiotherapy;A Coarse-to-Fine Framework for Mid-Radiotherapy Head and Neck Cancer MRI Segmentation;Assessing Self-supervised xLSTM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications;MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble;Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet;Benchmark of Deep Encoder-Decoder Architectures for Head and Neck Tumor Segmentation in Magnetic Resonance images: Contribution to the HNTSMRG Challenge;ensemble of LinkNet Networks for Head and Neck Tumor Segmentation;Enhancing nnUNetv2 Training with Autoencoder Architecture for Improved medical image Segmentation;head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet.

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13th international Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

13th International Workshop on Statistical Atlases and Compu...

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13th international Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

ISBN: (纸本)9783031234422

the proceedings contain 48 papers. the special focus in this conference is on Statistical Atlases and Computational Models of the Heart. the topics include: An Atlas-Based Analysis of Biventricular Mechanics in Tetralogy of Fallot;Review of Data Types and Model Dimensionality for Cardiac DTI SMS-Related Artefact Removal;improving Echocardiography Segmentation by Polar Transformation;spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach;interpretable Prediction of Post-Infarct Ventricular Arrhythmia Using Graph Convolutional Network;Unsupervised Echocardiography Registration through Patch-Based MLPs and Transformers;sensitivity Analysis of Left Atrial Wall Modeling Approaches and Inlet/Outlet Boundary Conditions in Fluid Simulations to Predict thrombus Formation;APHYN-EP: Physics-Based Deep Learning Framework to Learn and Forecast Cardiac Electrophysiology Dynamics;unsupervised Machine Learning Exploration of Morphological and Haemodynamic Indices to Predict thrombus Formation in the Left Atrial Appendage;learning Correspondences of Cardiac Motion from images Using Biomechanics-Informed Modeling;geometrical Deep Learning for the Estimation of Residence Time in the Left Atria;explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection;A Systematic Study of Race and Sex Bias in CNN-Based Cardiac MR Segmentation;mesh U-Nets for 3D Cardiac Deformation Modeling;skeletal Model-Based Analysis of the Tricuspid Valve in Hypoplastic Left Heart Syndrome;simplifying Disease Staging Models into a Single Anatomical Axis - A Case Study of Aortic Coarctation In-utero;point2Mesh-Net: Combining Point Cloud and Mesh-Based Deep Learning for Cardiac Shape Reconstruction;post-Infarction Risk Prediction with Mesh Classification Networks;statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries;computerized Analysis of the Human Heart to Guide Targeted Treatment of Atrial Fibrillation;Multi-modal Latent-

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge,...

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

ISBN: (纸本)9783030982522

the proceedings contain 30 papers. the special focus in this conference is on Head and Neck Tumor Segmentation. the topics include: Comparing Deep Learning and Conventional Machine Learning for Outcome Prediction of Head and Neck Cancer in PET/CT;A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET images;self-supervised Multi-modality image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer;Combining Tumor Segmentation Masks with PET/CT images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma;Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data;an Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data;a Hybrid Radiomics Approach to Modeling Progression-Free Survival in Head and Neck Cancers;deep Supervoxel Segmentation for Survival Analysis in Head and Neck Cancer Patients;Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT images and Application to Survival Risk Prediction;Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks;head and Neck Primary Tumor Segmentation Using Deep Neural Networks and Adaptive Ensembling;fusion-Based Head and Neck Tumor Segmentation and Survival Prediction Using Robust Deep Learning Techniques and Advanced Hybrid Machine Learning Systems;CCUT-Net: Pixel-Wise Global Context Channel Attention UT-Net for Head and Neck Tumor Segmentation;advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer;priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation;preface.

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4th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2020 and 1st international Workshop on Topological Data Analysis and Its Applications for medical Data, TDA4medicalData 2021 held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

4th International Workshop on Interpretability of Machine In...

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4th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2020 and 1st international Workshop on Topological Data Analysis and Its Applications for medical Data, TDA4medicalData 2021 held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030874438

the proceedings contain 12 papers. the special focus in this conference is on Interpretability of Machine Intelligence in medical image computing. the topics include: Statistical Modeling of Pulmonary Vasculatures with Topological Priors in CT Volumes;topological Detection of Alzheimer’s Disease Using Betti Curves;soft Attention Improves Skin Cancer Classification Performance;Deep Grading Based on Collective Artificial Intelligence for AD Diagnosis and Prognosis;this Explains that: Congruent image–Report Generation for Explainable medical image Analysis with Cyclic Generative Adversarial Networks;visual Explanation by Unifying Adversarial Generation and Feature Importance Attributions;the Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data;voxel-Level Importance Maps for Interpretable Brain Age Estimation;lattice Paths for Persistent Diagrams.

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1st Workshop on Deep Generative Models for medical image computing and computer assisted intervention, DGM4MICCAI 2021 and 1st Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021 held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

1st Workshop on Deep Generative Models for Medical Image Com...

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1st Workshop on Deep Generative Models for medical image computing and computer assisted intervention, DGM4MICCAI 2021 and 1st Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021 held in conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030882099

the proceedings contain 27 papers. the special focus in this conference is on Deep Generative Models for medical image computing and computer assisted intervention. the topics include: Improved Heatmap-Based Landmark Detection;cross-Domain Landmarks Detection in Mitral Regurgitation;scalable Semi-supervised Landmark Localization for X-ray images Using Few-Shot Deep Adaptive Graph;semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency;one-Shot Learning for Landmarks Detection;compound Figure Separation of Biomedical images with Side Loss;data Augmentation with Variational Autoencoders and Manifold Sampling;medical image Segmentation with Imperfect 3D Bounding Boxes;automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal images;ultrasound Variational Style Transfer to Generate images Beyond the Observed Domain;how Few Annotations are Needed for Segmentation Using a Multi-planar U-Net?;FS-Net: A New Paradigm of Data Expansion for medical image Segmentation;An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning;evaluation of Active Learning Techniques on medical image Classification with Unbalanced Data Distributions;Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation;label Noise in Segmentation Networks: Mitigation Must Deal with Bias;DeepMCAT: Large-Scale Deep Clustering for medical image Categorization;metaHistoSeg: A Python Framework for Meta Learning in Histopathology image Segmentation;3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of three-Dimensional medical images;bridging the Gap Between Paired and Unpaired medical image Translation;conditional Generation of medical images via Disentangled Adversarial Inference;CT-SGAN: Computed Tomography Synthesis GAN;hierarchical Probabilistic Ultrasound image Inpainting via Variational Inference.

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4th international Workshop on Machine Learning for medical image Reconstruction, MLMIR 2021 held in Conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

4th International Workshop on Machine Learning for Medical I...

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4th international Workshop on Machine Learning for medical image Reconstruction, MLMIR 2021 held in Conjunction with 24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030885519

the proceedings contain 13 papers. the special focus in this conference is on Machine Learning for medical image Reconstruction. the topics include: Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in Fluoroscopic Imaging;a Frequency Domain Constraint for Synthetic and Real X-ray image Super Resolution;semi- and Self-supervised Multi-view Fusion of 3D Microscopy images Using Generative Adversarial Networks;efficient image Registration Network for Non-Rigid Cardiac Motion Estimation;Evaluation of the Robustness of Learned MR image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge;Self-supervised Dynamic MRI Reconstruction;A Simulation Pipeline to Generate Realistic Breast images for Learning DCE-MRI Reconstruction;Deep MRI Reconstruction with Generative Vision Transformers;Distortion Removal and Deblurring of Single-Shot DWI MRI Scans;One Network to Solve them All: A Sequential Multi-task Joint Learning Network Framework for MR Imaging Pipeline;Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI.

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24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

24th International Conference on Medical Image Computing and...

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24th international conference on medical image computing and computer assisted intervention, MICCAI 2021

ISBN: (纸本)9783030872366

the proceedings contain 531 papers. the special focus in this conference is on medical image computing and computer assisted intervention. the topics include: TransBTS: Multimodal Brain Tumor Segmentation Using Transformer;automatic Polyp Segmentation via Multi-scale Subtraction Network;patch-Free 3D medical image Segmentation Driven by Super-Resolution Technique and Self-Supervised Guidance;progressively Normalized Self-Attention Network for Video Polyp Segmentation;SGNet: Structure-Aware Graph-Based Network for Airway Semantic Segmentation;NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale;AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions;improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects;carveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation;TransFuse: Fusing Transformers and CNNs for medical image Segmentation;boundary-Aware Transformers for Skin Lesion Segmentation;A Topological-Attention ConvLSTM Network and Its Application to EM images;BiX-NAS: Searching Efficient Bi-directional Architecture for medical image Segmentation;multi-task, Multi-domain Deep Segmentation with Shared Representations and Contrastive Regularization for Sparse Pediatric Datasets;TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations;learning Consistency- and Discrepancy-Context for 2D Organ Segmentation;partially-Supervised Learning for Vessel Segmentation in Ocular images;unsupervised Network Learning for Cell Segmentation;MT-UDA: Towards Unsupervised Cross-modality medical image Segmentation with Limited Source Labels;context-Aware Virtual Adversarial Training for Anatomically-Plausible Segmentation;Pancreas CT Segmentation by Predictive Phenotyping;interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces;multi-compound Transformer for Accurate Biomedical image Segmentation;kCBAC-Net: Deeply Supervised

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