Graphs in Biomedical Image Analysis : 6th International Workshop, GRAIL 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings

Anees Kazi

ISBN 10: 3031832426 / ISBN 13: 9783031832420

Verlag: Springer Nature Switzerland, Springer Nature Switzerland, 2025

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 6th International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2024, held in conjunction with MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024. The 12 full papers included in this volume were carefully reviewed and selected from 19 submissions.The papers cover a wide range of topics, such as deep/machine learning on graphs; probabilistic graphical models for biomedical data analysis; signal processing on graphs for biomedical image analysis; explainable AI (XAI) methods in geometric deep learning; big data analysis with graphs; graphs for small data sets; semantic graph research in medicine; modeling and applications of graph symmetry/equivariance; or graph generative models.

Bestandsnummer des Verkäufers 9783031832420

Bibliografische Details

• Titel: Graphs in Biomedical Image Analysis : 6th International Workshop, GRAIL 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings
• Verlag: Springer Nature Switzerland, Springer Nature Switzerland
• Veröffentlichungsdatum: 2025
• Sprache: Englisch
• ISBN-10: 3031832426
• ISBN-13: 9783031832420
• Bindung: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

This book constitutes the refereed proceedings of the 6th International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2024, held in conjunction with MICCAI 2024, in Marrakesh, Morocco, on October 6, 2024. The 12 full papers included in this volume were carefully reviewed and selected from 19 submissions.

The papers cover a wide range of topics, such as deep/machine learning on graphs; probabilistic graphical models for biomedical data analysis; signal processing on graphs for biomedical image analysis; explainable AI (XAI) methods in geometric deep learning; big data analysis with graphs; graphs for small data sets; semantic graph research in medicine; modeling and applications of graph symmetry/equivariance; or graph generative models.