Domain Adaptation and Representation Transfer: 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October ... (Lecture Notes in Computer Science, 14293). Questo articolo non è disponibile.
Lingua: inglese
Editore: Springer, 2023
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- Nuovo
Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Descrizione dell’articolo da parte del venditore
In English.
Codice articolo ria9783031458569_new
- Titolo
- Domain Adaptation and Representation Transfer: 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October ... (Lecture Notes in Computer Science, 14293)
- Editore
- Springer
- Anno di pubblicazione
- 2023
- Condizione
- New
- Rilegatura
- Brossura
- Lingua
- inglese
- ISBN 10
- 3031458567
- ISBN 13
- 9783031458569
- Peso dell'articolo
- 385 grammi
This book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023.
The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.
The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.
"Riassunto" può appartenere a un’altra edizione di questo titolo.
Dalla quarta di copertina
This book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023.
The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.
The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.
"Descrizione articolo" può appartenere a un’altra edizione di questo titolo.
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