Foundation Models for General Medical AI : Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings

Zhongying Deng

ISBN 10: 303173470X / ISBN 13: 9783031734700

Editore: Springer International Publishing, Springer Nature Switzerland, 2024

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Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings from theSecond International Workshop on Foundation Models for General Medical AI, MedAGI 2024, held in conjunction with the 27th International conference onMedical Image Computing and Computer Assisted Intervention, MICCAI 2024, inMarrakesh, Morocco in October 2024.The 17 papers included in this book were carefully reviewed and selected from 26 submissions. These papers provide insights into the current landscape of medical AI andfoundation models, that can pave the way for the evolution of task-specific medical AI systems into more generalized frameworks capable of tackling a diverse range of tasks, datasets, and domains.

Codice articolo 9783031734700

Dati bibliografici

• Titolo: Foundation Models for General Medical AI : Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings
• Editore: Springer International Publishing, Springer Nature Switzerland
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 303173470X
• ISBN 13: 9783031734700
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 11 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 11 millimetri di profondità
• Peso: 289 grammi

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Riassunto

This book constitutes the refereed proceedings from the Second International Workshop on Foundation Models for General Medical AI, MedAGI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024.

The 17 papers included in this book were carefully reviewed and selected from 26 submissions. These papers provide insights into the current landscape of medical AI and foundation models, that can pave the way for the evolution of task-specific medical AI systems into more generalized frameworks capable of tackling a diverse range of tasks, datasets, and domains.