Verlag: Springer Nature Switzerland, Springer International Publishing Sep 2022, 2022
ISBN 10: 3031167597 ISBN 13: 9783031167591
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. Neuware -This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 256 pp. Englisch.
Verlag: Springer Nature Switzerland, 2022
ISBN 10: 3031167597 ISBN 13: 9783031167591
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
Verlag: Springer Nature Switzerland, 2022
ISBN 10: 3031167597 ISBN 13: 9783031167591
Sprache: Englisch
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Medical Image Learning with Limited and Noisy Data | First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings | Ghada Zamzmi (u. a.) | Taschenbuch | xi | Englisch | 2022 | Springer Nature Switzerland | EAN 9783031167591 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Verlag: Springer-Nature New York Inc, 2023
ISBN 10: 3031471962 ISBN 13: 9783031471964
Sprache: Englisch
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 178,21
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 281 pages. 9.25x6.10x0.60 inches. In Stock.
Taschenbuch. Zustand: Neu. Medical Image Learning with Limited and Noisy Data | Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings | Zhiyun Xue (u. a.) | Taschenbuch | xi | Englisch | 2023 | Springer | EAN 9783031471964 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Verlag: Springer Nature Switzerland, Springer Nature Switzerland Okt 2023, 2023
ISBN 10: 3031471962 ISBN 13: 9783031471964
Sprache: Englisch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. Neuware -This book consists of full papers presented in the 2nd workshop of ¿Medical Image Learning with Noisy and Limited Data (MILLanD)¿ held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch.
Verlag: Springer Nature Switzerland, Springer Nature Switzerland, 2023
ISBN 10: 3031471962 ISBN 13: 9783031471964
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book consists of full papers presented in the 2nd workshop of 'Medical Image Learning with Noisy and Limited Data (MILLanD)' held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023).The 24 full papers presented were carefully reviewed and selected from 38 submissions.The conference focused onchallenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.