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 (Lecture Notes in Computer Science)

個数：

ポイントキャンペーン

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 (Lecture Notes in Computer Science)

Zhuang, XiahaiEDT/ Ding, WangbinEDT/ Liu, YuanyeEDT

ウェブストア価格 ¥16,415（本体¥14,923）
Springer, Berlin（2026/02発売）
外貨定価 EUR 62.05
【ウェブストア限定】洋書・洋古書ポイント5倍対象商品（～2/28）
ポイント 745pt

在庫がございません。海外の書籍取次会社を通じて出版社等からお取り寄せいたします。
通常6～9週間ほどで発送の見込みですが、商品によってはさらに時間がかかることもございます。
【重要ご説明事項】
1. 納期遅延や、ご入手不能となる場合がございます。
2. 複数冊ご注文の場合は、ご注文数量が揃ってからまとめて発送いたします。
3. 美品のご指定は承りかねます。

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版
商品コード 9783032162700

Full Description

This book constitutes the proceedings of the Second MICCAI Challenge, CARE 2025, held in Conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.

The 23 full papers in this book were carefully reviewed and selected from 51 submissions.

These tracks aim to advance real-world medical image computing by bridging the gap between AI model development and practical clinical applications.

.- 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.