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Foundation Models for General Medical AI : Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings (Lecture Notes in Computer Science)

By: Angelica I. Aviles-Rivero (Edited by) , Hyunwoo J. Kim (Edited by) , Junjun He (Edited by) , Shaoting Zhang (Edited by) , Won-Ki Jeong (Edited by) , Yiqing Shen (Edited by) , Zhongying Deng (Edited by)

Extended Catalogue

Ksh 10,250.00

Format: Paperback or Softback

ISBN-10: 303173470X

ISBN-13: 9783031734700

Collection / Series: Lecture Notes in Computer Science

Collection Type: Publisher collection

Edition statement: 2025 ed.

Publisher: Springer International Publishing AG

Imprint: Springer International Publishing AG

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Oct 3rd, 2024

Publication Status: Active

Product extent: 174 Pages

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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.

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.


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