Human-AI Collaboration (eBook, PDF)

First International Workshop, HAIC 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings
Redaktion: Guo, Xiaoqing; Vedula, S. Swaroop; Sahu, Manish; Ouyang, Cheng; Men, Qianhui; Lamdouar, Hala; Jin, Yueming

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the First International Workshop, HAIC 2025, held in Conjunction with MICCAI 2025, Daejeon, South Korea, in September 27, 2025.

The 9 full papers presented in this book were carefully selected and reviewed from 12 submissions. These papers have been organized in the following topical sections:

Medical image computing; computer-assisted intervention; human-ai collaboration; human-computer interaction; human factor modeling; medical image analysis.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 104
• Erscheinungstermin: 29. Oktober 2025
• Englisch
• ISBN-13: 9783032089700
• Artikelnr.: 75776726

Inhaltsangabe

.
Design and assessment for joint systems and workflows. .
Beyond Manual Annotation: A Human
AI Collaborative Framework for Medical Image Segmentation Using Only “Better or Worse” Expert Feedback. .
A methodology for clinically driven interactive segmentation evaluation. .
Interactive environments for clinical training, education,and human
AI teaming. .
Explainable AI for Automated User
specific Feedback in Surgical Skill Acquisition. .
Real
Time, Dynamic, and Highly Generalizable Ultrasound Image Simulation
Guided Procedure Training System for Musculoskeletal Minimally Invasive Treatment. .
Human
in
the
loop model training. .
Learning What is Worth Learning: Active and Sequential Domain Adaptation for Multi
modal Gross Tumor Volume Segmentation. .
Guided Active Learning for Medical Image Segmentation. .
Applications of human
AI interaction, collaboration, and human factor analysis. .
User Perception of Attention Visualizations: Effects on Interpretability Across Evidence
Based Medical Documents. .
Simulating Inter
observer Variability Across Clinical Experience Levels. .
Boosting transparency, interpretability, and risk management. .
Perceptual Evaluation of GANs and Diffusion Models for Generating X
rays.