- Neu
Guo / Jin / Lamdouar Human-AI Collaboration
Erscheinungsjahr 2025
ISBN: 978-3-032-08970-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
First International Workshop, HAIC 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings
E-Book, Englisch, 104 Seiten
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-032-08970-0
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
.- 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.
