Intrapartum Ultrasound: MICCAI 2025 Grand Challenge, IUGC 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings

Author:   Jieyun Bai ,  Yuxin Huang ,  Isaac Khobo ,  Mohammad Yaqub
Publisher:   Springer Nature Switzerland AG
ISBN:  

9783032116154


Pages:   120
Publication Date:   03 January 2026
Format:   Paperback
Availability:   In Print   Availability explained
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Intrapartum Ultrasound: MICCAI 2025 Grand Challenge, IUGC 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings


Overview

This book constitutes the proceedings of the MICCAI 2025 Grand Challenge on Intrapartum Ultrasound, IUGC 2025, which was held in conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025. The 9 full papers 1 short paper and  presented in this volume were carefully reviewed and selected from 20 submissions.  They are grouped into the following topics:  Top-performing solutions explored semi-supervised frameworks; Self-supervised pretraining; Transformer-based backbones; Adversarial learning; Pseudo-labeling strategies.

Full Product Details

Author:   Jieyun Bai ,  Yuxin Huang ,  Isaac Khobo ,  Mohammad Yaqub
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032116154


ISBN 10:   3032116155
Pages:   120
Publication Date:   03 January 2026
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

.- Noisy Student-Based Self-Training Enhances Landmark Detection in Intrapartum Ultrasound. .- Unlabeled Data-Driven Fetal Landmark Detection in Intrapartum Ultrasound. .- SSL-FetalBioNet: Self-Supervised Learning for Automated Angle of Progression Measurement in Intrapartum Ultrasound. .- DSNT-DeepUNet: A Coordinate Prediction Method for Intrapartum Ultrasound. .- Adversarially Fine-tuned Self-Supervised Framework for Automated Landmark Detection in Intrapartum Ultrasound. .- Progressive Semi-supervised Landmark Detection Algorithm for Intrapartum Ultrasound Measurement. .- Pseudo-label Enhanced TransUNet for Robust Landmark Localization in Intrapartum Ultrasound. .- A Two-Stage Semi-Supervised Ensemble Framework for Automated Angle of Progression Measurement in Intrapartum Ultrasound. .- GRM Framework. .- Heatmap Regression for Automated Angle of Progression Measurement: The Baseline Method for the IUGC2025.                                                                                                                                                                                   

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