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

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention, DGM4MICCAI 2023 Held in Conjunction with 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

3rd Workshop on Deep Generative Models for Medical Image Com...

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3rd Workshop on Deep Generative Models for medical image computing and computer assisted intervention, DGM4MICCAI 2023 Held in Conjunction with 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

ISBN: (纸本)9783031537660

The proceedings contain 23 papers. The special focus in this conference is on Deep Generative Models for medical image computing and computer assisted intervention. The topics include: Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model;A 3D Generative Model of Pathological Multi-modal MR images and Segmentations;Rethinking a Unified Generative Adversarial Model for MRI Modality Completion;diffusion Models for Generative Histopathology;shape-Guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature;pre-training with Diffusion Models for Dental Radiography Segmentation;ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space;ultrasound image Reconstruction with Denoising Diffusion Restoration Models;privacy Distillation: Reducing Re-identification Risk of Diffusion Models;Diffusion Model Based Knee Cartilage Segmentation in MRI;Semantic image Synthesis for Abdominal CT;CT Reconstruction from Few Planar X-Rays with Application Towards Low-Resource Radiotherapy;Reference-Free Isotropic 3D EM Reconstruction Using Diffusion Models;federated Multimodal and Multiresolution Graph Integration for Connectional Brain Template Learning;metrics to Quantify Global Consistency in Synthetic medical images;MIM-OOD: Generative Masked image Modelling for Out-of-Distribution Detection in medical images;towards Generalised Neural Implicit Representations for image Registration;investigating Data Memorization in 3D Latent Diffusion Models for medical image Synthesis;ViT-DAE: Transformer-Driven Diffusion Autoencoder for Histopathology image Analysis;anomaly Guided Generalizable Super-Resolution of Chest X-Ray images Using Multi-level Information Rendering;importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentation.

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3rd Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2024 was held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

3rd Workshop on Computational Mathematics Modeling in Cancer...

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3rd Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2024 was held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031733598

The proceedings contain 12 papers. The special focus in this conference is on Computational Mathematics Modeling in Cancer Analysis. The topics include: Follicular Lymphoma Grading Based on 3D-DDcGAN and Bayesian CNN Using PET-CT images;Multi-channel Multi-model Fusion Module (MMFM) Based Circulating Abnormal Cells (CACs) Detection for Lung Cancer Early Diagnosis with Fluorescence in Situ Hybridization (FISH) images;domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation;Attention-Fusion Model for Multi-omics (AMMO) Data Integration in Lung Adenocarcinoma;PD-L1 Expression Prediction Using Scalable Multi Instance Transformer;improving Single-Source Domain Generalization via Anatomy-Guided Texture Augmentation for Cervical Tumor Segmentation;PANDA: Pneumonitis Anomaly Detection Using Attention U-Net;estimating the Average Treatment Effect Using Weighting Methods in Lung Cancer Immunotherapy;Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging;assessment of Radiomics Feature Repeatability and Reproducibility and Their Generalizability Across image Modalities by Perturbation in Nasopharyngeal Carcinoma Patients.

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

3rd International Workshop on Applications of Medical Artifi...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031820069

The proceedings contain 24 papers. The special focus in this conference is on Applications of medical Artificial Intelligence. The topics include: SP-NAS: Surgical Phase Recognition-Based Navigation Adjustment System for Distal Gastrectomy;transforming Multimodal Models into Action Models for Radiotherapy;enhanced Interpretability in Histopathological images via Combined Tissue and Cell-Level Graph Analysis;targeted Visual Prompting for medical Visual Question Answering;deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver;predicting Falls Through Muscle Weakness from a Single Whole Body image: A Multimodal Contrastive Learning Framework;Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools;source Matters: Source Dataset Impact on Model Robustness in medical Imaging;evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia;incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts;assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears;automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery;quantifying Knee Cartilage Shape and Lesion: From image to Metrics;RadimageGAN – A Multi-modal Dataset-Scale Generative AI for medical Imaging;Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls;SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury;EHRmonize: A Framework for medical Concept Abstraction from Electronic Health Records using Large Language Models;evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking;normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification;one-Shot medical

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3rd international Workshop on Cancer Prevention, detection and intervention, CaPTion 2024, held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

3rd International Workshop on Cancer Prevention, detection a...

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3rd international Workshop on Cancer Prevention, detection and intervention, CaPTion 2024, held in Conjunction with 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

ISBN: (纸本)9783031733758

The proceedings contain 22 papers. The special focus in this conference is on Cancer Prevention, detection and intervention. The topics include: Classification of Endoscopy and Video Capsule images Using CNN-Transformer Model;multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases;seeing More with Less: Meta-learning and Diffusion Models for Tumor Characterization in Low-Data Settings;performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification;on Undesired Emergent Behaviors in Compound Prostate Cancer Detection Systems;optimizing Multi-expert Consensus for Classification and Precise Localization of Barrett’s Neoplasia;Automated Hepatocellular Carcinoma Analysis in Multi-phase CT with Deep Learning;Refining Deep Learning Segmentation Maps with a Local Thresholding Approach: Application to Liver Surface Nodularity Quantification in CT;Uncertainty-Aware Deep Learning Classification for MRI-Based Prostate Cancer Detection;Generalized Polyp Detection from Colonoscopy Frames Using Proposed EDF-YOLO8 Network;AI-assisted Laryngeal Examination System;UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision;SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labeling;AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients;deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery;treatment Efficacy Prediction of Focused Ultrasound Therapies Using Multi-parametric Magnetic Resonance Imaging;SurRecNet: A Multi-task Model with Integrating MRI and Diagnostic Descriptions for Rectal Cancer Survival Analysis;improved Prediction of Recurrence After Prostate Cancer Radiotherapy Using Multimodal Data and in Silico simulations;autoDoseRank: Automated Dosimetry-Informed Segmentation Ranking for Radiotherapy

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in...

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3rd Diabetic Foot Ulcers Grand Challenge, DFUC 2022, held in Conjunction with the 25th international conference on medical image computing and computer-assisted intervention, MICCAI 2022

ISBN: (纸本)9783031263538

The proceedings contain 10 papers. The special focus in this conference is on Diabetic Foot Ulcers Grand Challenge. The topics include: HardNet-DFUS: Enhancing Backbone and Decoder of HardNet-MSEG for Diabetic Foot Ulcer image Segmentation;OCRNet for Diabetic Foot Ulcer Segmentation Combined with Edge Loss;on the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness;capture the Devil in the Details via Partition-then-Ensemble on Higher Resolution images;unconditionally Generated and Pseudo-Labeled Synthetic images for Diabetic Foot Ulcer Segmentation Dataset Extension;diabetic Foot Ulcer Segmentation Using Convolutional and Transformer-Based Models;refined Mixup Augmentation for Diabetic Foot Ulcer Segmentation;DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framework with Vision Transformer Based Detection.

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3rd international Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with 26th international conference on medical image computing and computer-assisted intervention, MICCAI 2023

3rd International Challenge on Kidney and Kidney Tumor Segme...

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3rd international Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with 26th international conference on medical image computing and computer-assisted intervention, MICCAI 2023

ISBN: (纸本)9783031548055

The proceedings contain 22 papers. The special focus in this conference is on Kidney and Kidney Tumor Segmentation. The topics include: Cascaded nnU-Net for Kidney and Kidney Tumor Segmentation;Cascade UNets for Kidney and Kidney Tumor Segmentation;3d U-Net with ROI Segmentation of Kidneys and Masses in CT Scans;two-Stage Segmentation Framework with Parallel Decoders for the Kidney and Kidney Tumor Segmentation;Genetic Algorithm Enhanced nnU-Net for the MICCAI KiTS23 Challenge;GSCA-Net: A Global Spatial Channel Attention Network for Kidney, Tumor and Cyst Segmentation;Two-Stage Segmentation and Ensemble Modeling: Kidney Tumor Analysis in CT images;using Uncertainty Information for Kidney Tumor Segmentation;an Ensemble of 2.5D ResUnet Based Models for Segmentation of Kidney and Masses;a Hybrid Network Based on nnU-Net and Swin Transformer for Kidney Tumor Segmentation;Analyzing Domain Shift When Using Additional Data for the MICCAI KiTS23 Challenge;dynamic Resolution Network for Kidney Tumor Segmentation;Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge;3D Segmentation of Kidneys, Kidney Tumors and Cysts on CT images - KiTS23 Challenge;Exploring 3D U-Net Training Configurations and Post-processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge;preface;leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney Cysts;Deep Learning-Based Hierarchical Delineation of Kidneys, Tumors, and Cysts in CT images;kidney and Kidney Tumor Segmentation via Transfer Learning;Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge.

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

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

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

ISBN: (纸本)9783031274190

The proceedings contain 24 papers. The special focus in this conference is on 3D Head and Neck Tumor Segmentation in PET/CT Challenge. The topics include: Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients;Combining nnUNet and AutoML for Automatic Head and Neck Tumor Segmentation and Recurrence-Free Survival Prediction in PET/CT images;Automated Head and Neck Tumor Segmentation from 3D PET/CT HECKTOR 2022 Challenge Report;Head and Neck Cancer Localization with Retina Unet for Automated Segmentation and Time-To-Event Prognosis from PET/CT images;HNT-AI: An Automatic Segmentation Framework for Head and Neck Primary Tumors and Lymph Nodes in FDG- PET/CT images;Head and Neck Tumor Segmentation with 3D UNet and Survival Prediction with Multiple Instance Neural Network;Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer;Deep Learning and Radiomics Based PET/CT image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients;A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET images;A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/CT images;octree Boundary Transfiner: Efficient Transformers for Tumor Segmentation Refinement;Head and Neck Primary Tumor and Lymph Node Auto-segmentation for PET/CT Scans;fusion-Based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques;stacking Feature Maps of Multi-scaled medical images in U-Net for 3D Head and Neck Tumor Segmentation;a Fine-Tuned 3D U-Net for Primary Tumor and Affected Lymph Nodes Segmentation in Fused Multimodal images of Oropharyngeal Cancer;preface;Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT;multi-scale Fusion Methodologies for Head and Neck

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3rd international Workshop on Multiscale Multimodal medical Imaging, MMMI 2022, held in conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

3rd International Workshop on Multiscale Multimodal Medical ...

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3rd international Workshop on Multiscale Multimodal medical Imaging, MMMI 2022, held in conjunction with the 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

ISBN: (纸本)9783031188138

The proceedings contain 12 papers. The special focus in this conference is on Multiscale Multimodal medical Imaging. The topics include: Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation;Improved Multi-modal Patch Based Lymphoma Segmentation with Negative Sample Augmentation and Label Guidance on PET/CT Scans;visual Modalities Based Multimodal Fusion for Surgical Phase Recognition;cross-Scale Attention Guided Multi-instance Learning for Crohn’s Disease Diagnosis with Pathological images;vessel Segmentation via Link Prediction of Graph Neural Networks;A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset;Liver Segmentation Quality Control in Multi-sequence MR Studies;Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI;gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical image Segmentation;Learning-Based Detection of MYCN Amplification in Clinical Neuroblastoma Patients: A Pilot Study.

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3rd international Workshop of Advances in Simplifying medical Ultrasound, ASMUS 2022, held in Conjunction with 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

3rd International Workshop of Advances in Simplifying Medica...

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3rd international Workshop of Advances in Simplifying medical Ultrasound, ASMUS 2022, held in Conjunction with 25th international conference on medical image computing and computer assisted intervention, MICCAI 2022

ISBN: (纸本)9783031169014

The proceedings contain 18 papers. The special focus in this conference is on Advances in Simplifying medical Ultrasound. The topics include: Adnexal Mass Segmentation with Ultrasound Data Synthesis;self-Knowledge Distillation for First Trimester Ultrasound Saliency Prediction;a Universal End-to-End Description of Pulse-Echo Ultrasound image Reconstruction;learning Generalized Non-rigid Multimodal Biomedical image Registration from Generic Point Set Data;contact Force Prediction for a Robotic Transesophageal Ultrasound Probe via Operating Torque Sensing;meta-registration: Learning Test-Time Optimization for Single-Pair image Registration;automatic Quality Assessment of First Trimester Crown-Rump-Length Ultrasound images;towards Multi-modal Self-supervised Video and Ultrasound Pose Estimation for Laparoscopic Liver Surgery;Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures;RL Based Unsupervised Video Summarization Framework for Ultrasound Imaging;prediction of Kidney Transplant Function with Machine Learning from Computational Ultrasound Features;differential Learning from Sparse and Noisy Labels for Robust Detection of Clinical Landmarks in Echo Cine Series;end-to-End Myocardial Infarction Classification from Echocardiographic Scans;view Classification of Color Doppler Echocardiography via Automatic Alignment Between Doppler and B-Mode Imaging;AI-Enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography;efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D Echo Views.

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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

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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