Hongyi Shen | Akateeminen Kirjakauppa

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FOUNDATION MODELS FOR GENERAL MEDICAL AI - SECOND INTERNATIONAL WORKSHOP, MEDAGI 2024, HELD IN CONJUNCTION WITH MICCAI 2024, MAR

Zhongying Deng; Yiqing Shen; Hyunwoo J. Kim; Won-Ki Jeong; Angelica I. Aviles-Rivero; Junjun He; Shaoting Zhang
Springer International Publishing AG (2024)
Pehmeäkantinen kirja

49,60 €

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Hongyi Shen; Jie Ruan; Kejin Wu
Cnpiecsb (2016)
Pehmeäkantinen kirja

52,30 €

MULTISCALE METHODS FOR FREDHOLM INTEGRAL EQUATIONS

Zhongying Chen; Charles A. Micchelli; Yuesheng Xu
Cambridge University Press (2015)
Kovakantinen kirja

175,30 €

LIUTEX AND ITS APPLICATIONS IN TURBULENCE RESEARCH

Chaoqun Liu; Hongyi Xu; Xiaoshu Cai; Yisheng Gao
Elsevier Science Publishing Co Inc (2020)
Pehmeäkantinen kirja

119,90 €

ADVANCES IN COMPUTATIONAL MATHEMATICS

Zhongying Chen; Yueshen Li; Charles Micchelli; Yuesheng Xu
Taylor & Francis Inc (1998)
Pehmeäkantinen kirja

225,50 €

Foundation Models for General Medical AI - Second International Workshop, MedAGI 2024, Held in Conjunction with MICCAI 2024, Mar

Zhongying Deng, Yiqing Shen, Hyunwoo J. Kim, Won-Ki Jeong, Angelica I. Aviles-Rivero, Junjun He, Shaoting Zhang

49,60 €

Springer International Publishing AG
Sivumäärä: 174 sivua
Asu: Pehmeäkantinen kirja
Painos: 2025 ed.
Julkaisuvuosi: 2024, 03.10.2024 (lisätietoa)
Kieli: Englanti
Tuotesarja: Lecture Notes in Computer Science 15184

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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