Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Bibliographic Details

Title: Graphs in Biomedical Image Analysis (Paperback)
Publisher: Springer International Publishing AG, Cham
Publication Date: 2025
Language: English
ISBN 10: 3031832426
ISBN 13: 9783031832420
Binding: Paperback
Condition: new

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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.

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