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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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5th international Workshop on Advances in Simplifying medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

5th International Workshop on Advances in Simplifying Medica...

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5th international Workshop on Advances in Simplifying medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031736469

the proceedings contain 21 papers. the special focus in this conference is on Advances in Simplifying medical Ultrasound. the topics include: Do High-Performance image-to-image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer;PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement;PIPsUS: Self-supervised Point Tracking in Ultrasound;structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train;an Evaluation of Low-Cost Hardware on 3D Ultrasound Reconstruction Accuracy;Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound;automatic Facial Axes Standardization of 3D Fetal Ultrasound images;C-TRUS: A Novel Dataset and Initial Benchmark for Colon Wall Segmentation in Transabdominal Ultrasound;label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets with Domain Shift and Partial Labelling;introducing Anatomical Constraints in Mitral Annulus Segmentation in Transesophageal Echocardiography;interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images;Enhanced Uncertainty Estimation in Ultrasound image Segmentation with MSU-Net;multi-site Class-Incremental Learning with Weighted Experts in Echocardiography;Masked Autoencoders for medical Ultrasound Videos Using ROI-Aware Masking;uncertainty-Based Multi-modal Learning for Myocardial Infarction Diagnosis Using Echocardiography and Electrocardiograms;fetal Ultrasound Video Representation Learning Using Contrastive Rubik’s Cube Recovery;LoRIS - Weakly-Supervised Anomaly Detection for Ultrasound images;unsupervised Detection of Fetal Brain Anomalies Using Denoising Diffusion Models;diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound.

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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated Learning, DeCaF 2024 held at 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031776090

the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in medical image computing, Embodied AI and Robotics for Healthcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated Learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion images;a Vision Transformer with Adaptive Cross-image and Cross-Resolution Attention;lesion Elevation Prediction from Skin images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning with Distributed Heterogeneous Data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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5th Workshop on GRaphs in biomedical image anaLysis Satellite event at MICCAI, GRAIL 2023 and 1st Cell Detection from Cell-Tissue Interaction challenge in MICCAI, OCELOT 2023 Held in Conjunction with international conference on medical image computing and computer-assisted intervention, MICCAI 2023

5th Workshop on GRaphs in biomedicAl Image anaLysis Satellit...

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5th Workshop on GRaphs in biomedical image anaLysis Satellite event at MICCAI, GRAIL 2023 and 1st Cell Detection from Cell-Tissue Interaction challenge in MICCAI, OCELOT 2023 Held in Conjunction with international conference on medical image computing and computer-assisted intervention, MICCAI 2023

ISBN: (纸本)9783031550874

the proceedings contain 15 papers. the special focus in this conference is on GRaphs in biomedical image anaLysis Satellite. the topics include: Self Supervised Multi-view Graph Representation Learning in Digital Pathology;a Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression;tertiary Lymphoid Structures Generation through Graph-Based Diffusion;multi-head Graph Convolutional Network for Structural Connectome Classification;extended Graph Assessment Metrics for Regression and Weighted Graphs;enhancing Cell Detection in Histopathology images: A ViT-Based U-Net Approach;Enhancing Cell Detection via FC-HarDNet and Tissue Segmentation: OCELOT 2023 Challenge Approach;generating BlobCell Label from Weak Annotations for Precise Cell Segmentation;OCELOT 2023 Preface;GRAIL 2023 Preface;SCOPE: Structural Continuity Preservation for Retinal Vessel Segmentation;prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays;dense Prediction of Cell Centroids Using Tissue Context and Cell Refinement;detecting Cells in Histopathology images with a ResNet Ensemble Model.

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Workshop on Longitudinal Disease Tracking and Modeling with medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

Workshop on Longitudinal Disease Tracking and Modeling with ...

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Workshop on Longitudinal Disease Tracking and Modeling with medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031845246

the proceedings contain 21 papers. the special focus in this conference is on Longitudinal Disease Tracking and Modeling with medical images and Data. the topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: Application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular medical image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling;segHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-Based Coordinate System for the Human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting Human Brain States with Transformer;Cross-Modality image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal Learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry image Translation with Dual-Branch Pix2pix Generator;Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel Dataset and Deep-Learning Experiments;multimodality Frequency Feature Customized Learning for Pediatric Ventricu

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5th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2022, held in conjunction with the 25th international conference on medical Imaging and computer-assisted intervention, MICCAI 2022

5th International Workshop on Interpretability of Machine In...

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5th international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2022, held in conjunction with the 25th international conference on medical Imaging and computer-assisted intervention, MICCAI 2022

ISBN: (纸本)9783031179754

the proceedings contain 10 papers. the special focus in this conference is on Interpretability of Machine Intelligence in medical image computing. the topics include: Do Pre-processing and Augmentation Help Explainability? A Multi-seed Analysis for Brain Age Estimation;towards Self-explainable Transformers for Cell Classification in Flow Cytometry Data;reducing Annotation Need in Self-explanatory Models for Lung Nodule Diagnosis;attention-Based Interpretable Regression of Gene Expression in Histology;beyond Voxel Prediction Uncertainty: Identifying Brain Lesions You Can Trust;interpretable Vertebral Fracture Diagnosis;Multi-modal Volumetric Concept Activation to Explain Detection and Classification of Metastatic Prostate Cancer on PSMA-PET/CT;KAM - A Kernel Attention Module for Emotion Classification with EEG Data.

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5th international Workshop on Machine Learning for medical Reconstruction, MLMIR 2022, held in conjunction with 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

5th International Workshop on Machine Learning for Medical R...

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5th international Workshop on Machine Learning for medical Reconstruction, MLMIR 2022, held in conjunction with 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

ISBN: (纸本)9783031172465

the proceedings contain 15 papers. the special focus in this conference is on Machine Learning for medical Reconstruction. the topics include: PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction;learning While Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging;DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction;MTD-GAN: Multi-task Discriminator Based Generative Adversarial Networks for Low-Dose CT Denoising;Uncertainty-Informed Bayesian PET image Reconstruction Using a Deep image Prior;NPB-REC: Non-parametric Assessment of Uncertainty in Deep-Learning-Based MRI Reconstruction from Undersampled Data;Adversarial Robustness of MR image Reconstruction Under Realistic Perturbations;High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors;Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases;Segmentation-Aware MRI Reconstruction;MRI Reconstruction with Conditional Adversarial Transformers;A Noise-Level-Aware Framework for PET image Denoising;DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction.

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5th international Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

5th International Workshop on Predictive Intelligence in Med...

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5th international Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

ISBN: (纸本)9783031169182

the proceedings contain 19 papers. the special focus in this conference is on Predictive Intelligence in Medicine. the topics include: Discriminative Deep Neural Network for Predicting Knee OsteoArthritis in Early Stage;long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data;Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts;investigating the Predictive Reproducibility of Federated Graph Neural Networks Using medical Datasets;learning Subject-Specific Functional Parcellations from Cortical Surface Measures;a Triplet Contrast Learning of Global and Local Representations for Unannotated medical images;predicting Brain Multigraph Population from a Single Graph Template for Boosting One-Shot Classification;Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores Using Graph Neural Networks and Meta-learning;bridging the Gap Between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing;Multi-tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas;multiple Instance Neuroimage Transformer;intervertebral Disc Labeling with Learning Shape Information, a Look once Approach;Mixup Augmentation Improves Age Prediction from T1-Weighted Brain MRI Scans;Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning;MISS-Net: Multi-view Contrastive Transformer Network for MCI Stages Prediction Using Brain18 F-FDG PET Imaging;transDeepLab: Convolution-Free Transformer-Based DeepLab v3+ for medical image Segmentation;preface.

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5th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

5th International Workshop on Machine Learning in Clinical N...

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5th international Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

ISBN: (纸本)9783031178986

the proceedings contain 17 papers. the special focus in this conference is on Machine Learning in Clinical Neuroimaging. the topics include: Lifestyle Factors that Promote Brain Structural Resilience in Individuals with Genetic Risk Factors for Dementia;learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging;augmenting Magnetic Resonance Imaging with Tabular Features for Enhanced and Interpretable Medial Temporal Lobe Atrophy Prediction;automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injury;autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification be Improved by Dyadic Neural Biomarkers Using Unsupervised Graph Representation Learning?;fMRI-S4: Learning Short- and Long-Range Dynamic fMRI Dependencies Using 1D Convolutions and State Space Models;data Augmentation via Partial Nonlinear Registration for Brain-Age Prediction;A Study of Demographic Bias in CNN-Based Brain MR Segmentation;Volume is All You Need: Improving Multi-task Multiple Instance Learning for WMH Segmentation and Severity Estimation;self-supervised Test-Time Adaptation for medical image Segmentation;accurate Hippocampus Segmentation Based on Self-supervised Learning with Fewer Labeled Data;Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-Based Network;weakly Supervised Intracranial Hemorrhage Segmentation Using Hierarchical Combination of Attention Maps from a Swin Transformer;boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma;Neuroimaging Harmonization Using cGANs: image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency.

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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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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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