Comprehensive Analysis and Computing of Real-World Medical Images
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Comprehensive Analysis and Computing of Real-World Medical Images
Second MICCAI Challenge, CARE 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings
Zhao, Jichao; Zhuang, Xiahai; Liu, Yuanye; Ding, Wangbin; Ma, Yingliang; Wang, Bomin
Springer Nature Switzerland AG
03/2026
253
Mole
Inglês
9783032162700
Pré-lançamento - envio 15 a 20 dias após a sua edição
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.- A Unified 3D Cardiac Structure Segmentation Framework for Heterogeneous Medical Data.
.- Uncertainty-Guided Curriculum Learning for Automated Liver Fibrosis Staging on Heterogeneous MRI.
.- Uncertainty-Guided Hard-Soft Priors for Myocardial Scar and Edema Segmentation on Multi-Sequence CMR Images.
.- MA2: Unifying Modality-Agnostic Segmentation and Modality-Aware Staging for Real-World Liver Fibrosis Analysis.
.- Transfer Learning for Multimodal Whole Heart Segmentation Supported by Intensity Transformations.
.- CoSSeg-TTA: Contrast-Aware Semi-Supervised Segmentation with Domain Generalization and Test-Time Adaptation.
.- CardioSeqM: A Scalable and Context-Aware Model for Unified Heart Segmentation from Volumetric Cardiac Data.
.- A Latent-Guided Hybrid Architecture for Liver Segmentation in Contrast-Enhanced MRI.
.- IE-UNet: Implicit Neural Representation-Driven Whole Heart Segmentation.
,- Multi-Modal MRI Fusion for Liver Fibrosis Staging and Semi-Supervised Pipeline for Liver Segmentation.
.- Two-Stage Approach for Myocardial Scar and Edema Segmentation Using Synthetic Multi-Sequence MRI and Auxiliary Scar Prediction.
.- Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI.
.- A Two-stage Myocardial Pathology Segmentation Method Based on Multi-sequence CMR images.
.- Decoupled Teacher-Student Framework for Few-shot Liver Segmentation with Boundary-Aware Learning.
.- Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI.
.- UniCarSeg: A Unified Framework for Multi-Task Cardiac Image Segmentation.
.- Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation.
.- SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2.
.- Early Fusion-Based Multimodal Cardiac MRI Segmentation with Domain-Aware Augmentation.
.- Dual-Task Multi-Modal 2.5D Swin Transformer for Liver Fibrosis Staging.
.- EHU-Mamba2: Enhanced U-Mamba for Multi-Center Cardiac MR Segmentation with Dynamic Alignment and Adaptive Upsampling.
.- Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images.
.- Multi-b ranch Attention Network for Liver Fibrosis Staging in Multi-Phase MRI.
.- Uncertainty-Guided Curriculum Learning for Automated Liver Fibrosis Staging on Heterogeneous MRI.
.- Uncertainty-Guided Hard-Soft Priors for Myocardial Scar and Edema Segmentation on Multi-Sequence CMR Images.
.- MA2: Unifying Modality-Agnostic Segmentation and Modality-Aware Staging for Real-World Liver Fibrosis Analysis.
.- Transfer Learning for Multimodal Whole Heart Segmentation Supported by Intensity Transformations.
.- CoSSeg-TTA: Contrast-Aware Semi-Supervised Segmentation with Domain Generalization and Test-Time Adaptation.
.- CardioSeqM: A Scalable and Context-Aware Model for Unified Heart Segmentation from Volumetric Cardiac Data.
.- A Latent-Guided Hybrid Architecture for Liver Segmentation in Contrast-Enhanced MRI.
.- IE-UNet: Implicit Neural Representation-Driven Whole Heart Segmentation.
,- Multi-Modal MRI Fusion for Liver Fibrosis Staging and Semi-Supervised Pipeline for Liver Segmentation.
.- Two-Stage Approach for Myocardial Scar and Edema Segmentation Using Synthetic Multi-Sequence MRI and Auxiliary Scar Prediction.
.- Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI.
.- A Two-stage Myocardial Pathology Segmentation Method Based on Multi-sequence CMR images.
.- Decoupled Teacher-Student Framework for Few-shot Liver Segmentation with Boundary-Aware Learning.
.- Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI.
.- UniCarSeg: A Unified Framework for Multi-Task Cardiac Image Segmentation.
.- Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation.
.- SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2.
.- Early Fusion-Based Multimodal Cardiac MRI Segmentation with Domain-Aware Augmentation.
.- Dual-Task Multi-Modal 2.5D Swin Transformer for Liver Fibrosis Staging.
.- EHU-Mamba2: Enhanced U-Mamba for Multi-Center Cardiac MR Segmentation with Dynamic Alignment and Adaptive Upsampling.
.- Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images.
.- Multi-b ranch Attention Network for Liver Fibrosis Staging in Multi-Phase MRI.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Real world medical image analysis;multi-modality;multi-center;misalignment;generalizability;transferring foundation model;segemetation;staging;cardiac;liver
.- A Unified 3D Cardiac Structure Segmentation Framework for Heterogeneous Medical Data.
.- Uncertainty-Guided Curriculum Learning for Automated Liver Fibrosis Staging on Heterogeneous MRI.
.- Uncertainty-Guided Hard-Soft Priors for Myocardial Scar and Edema Segmentation on Multi-Sequence CMR Images.
.- MA2: Unifying Modality-Agnostic Segmentation and Modality-Aware Staging for Real-World Liver Fibrosis Analysis.
.- Transfer Learning for Multimodal Whole Heart Segmentation Supported by Intensity Transformations.
.- CoSSeg-TTA: Contrast-Aware Semi-Supervised Segmentation with Domain Generalization and Test-Time Adaptation.
.- CardioSeqM: A Scalable and Context-Aware Model for Unified Heart Segmentation from Volumetric Cardiac Data.
.- A Latent-Guided Hybrid Architecture for Liver Segmentation in Contrast-Enhanced MRI.
.- IE-UNet: Implicit Neural Representation-Driven Whole Heart Segmentation.
,- Multi-Modal MRI Fusion for Liver Fibrosis Staging and Semi-Supervised Pipeline for Liver Segmentation.
.- Two-Stage Approach for Myocardial Scar and Edema Segmentation Using Synthetic Multi-Sequence MRI and Auxiliary Scar Prediction.
.- Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI.
.- A Two-stage Myocardial Pathology Segmentation Method Based on Multi-sequence CMR images.
.- Decoupled Teacher-Student Framework for Few-shot Liver Segmentation with Boundary-Aware Learning.
.- Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI.
.- UniCarSeg: A Unified Framework for Multi-Task Cardiac Image Segmentation.
.- Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation.
.- SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2.
.- Early Fusion-Based Multimodal Cardiac MRI Segmentation with Domain-Aware Augmentation.
.- Dual-Task Multi-Modal 2.5D Swin Transformer for Liver Fibrosis Staging.
.- EHU-Mamba2: Enhanced U-Mamba for Multi-Center Cardiac MR Segmentation with Dynamic Alignment and Adaptive Upsampling.
.- Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images.
.- Multi-b ranch Attention Network for Liver Fibrosis Staging in Multi-Phase MRI.
.- Uncertainty-Guided Curriculum Learning for Automated Liver Fibrosis Staging on Heterogeneous MRI.
.- Uncertainty-Guided Hard-Soft Priors for Myocardial Scar and Edema Segmentation on Multi-Sequence CMR Images.
.- MA2: Unifying Modality-Agnostic Segmentation and Modality-Aware Staging for Real-World Liver Fibrosis Analysis.
.- Transfer Learning for Multimodal Whole Heart Segmentation Supported by Intensity Transformations.
.- CoSSeg-TTA: Contrast-Aware Semi-Supervised Segmentation with Domain Generalization and Test-Time Adaptation.
.- CardioSeqM: A Scalable and Context-Aware Model for Unified Heart Segmentation from Volumetric Cardiac Data.
.- A Latent-Guided Hybrid Architecture for Liver Segmentation in Contrast-Enhanced MRI.
.- IE-UNet: Implicit Neural Representation-Driven Whole Heart Segmentation.
,- Multi-Modal MRI Fusion for Liver Fibrosis Staging and Semi-Supervised Pipeline for Liver Segmentation.
.- Two-Stage Approach for Myocardial Scar and Edema Segmentation Using Synthetic Multi-Sequence MRI and Auxiliary Scar Prediction.
.- Semi-supervised Liver Segmentation and Patch-based Fibrosis Staging with Registration-aided Multi-parametric MRI.
.- A Two-stage Myocardial Pathology Segmentation Method Based on Multi-sequence CMR images.
.- Decoupled Teacher-Student Framework for Few-shot Liver Segmentation with Boundary-Aware Learning.
.- Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI.
.- UniCarSeg: A Unified Framework for Multi-Task Cardiac Image Segmentation.
.- Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation.
.- SSL-MedSAM2: A Semi-supervised Medical Image Segmentation Framework Powered by Few-shot Learning of SAM2.
.- Early Fusion-Based Multimodal Cardiac MRI Segmentation with Domain-Aware Augmentation.
.- Dual-Task Multi-Modal 2.5D Swin Transformer for Liver Fibrosis Staging.
.- EHU-Mamba2: Enhanced U-Mamba for Multi-Center Cardiac MR Segmentation with Dynamic Alignment and Adaptive Upsampling.
.- Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images.
.- Multi-b ranch Attention Network for Liver Fibrosis Staging in Multi-Phase MRI.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
