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In den WarenkorbZustand: Very Good. Lisa Ferrante (illustrator). Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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In den WarenkorbPAP. Zustand: New. Lisa Ferrante (illustrator). New Book. Shipped from UK. Established seller since 2000.
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In den WarenkorbUNK. Zustand: New. Lisa Ferrante (illustrator). New Book. Shipped from UK. Established seller since 2000.
Anbieter: medimops, Berlin, Deutschland
Zustand: very good. Caterina Ferrante (illustrator). Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.
Anbieter: medimops, Berlin, Deutschland
Zustand: as new. Caterina Ferrante (illustrator). Wie neu/Like new.
Anbieter: Dream Books Co., Denver, CO, USA
Zustand: good. Gently used with minimal wear on the corners and cover. A few pages may contain light highlighting or writing, but the text remains fully legible. Dust jacket may be missing, and supplemental materials like CDs or codes may not be included. May be ex-library with library markings. Ships promptly!
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In den WarenkorbZustand: New. Lisa Ferrante (illustrator). In.
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In den WarenkorbPaperback. Zustand: Brand New. Lisa Ferrante (illustrator). 96 pages. 6.69x4.92x0.16 inches. In Stock.
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In den WarenkorbZustand: New. Lisa Ferrante (illustrator). In.
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In den WarenkorbPaperback. Zustand: Brand New. Lisa Ferrante (illustrator). 220 pages. 8.98x7.01x0.79 inches. In Stock.
EUR 28,65
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In den WarenkorbPaperback. Zustand: Brand New. Lisa Ferrante (illustrator). 220 pages. 8.98x7.01x0.79 inches. In Stock.
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In den WarenkorbHardcover. Zustand: Brand New. har/psc edition. 96 pages. 6.75x6.75x0.75 inches. In Stock.
Zustand: New. Lisa Ferrante (illustrator).
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 60,68
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In den WarenkorbZustand: NEW. Caterina Ferrante (illustrator).
Buch. Zustand: Neu. Lisa Ferrante (illustrator). Neuware.
Sprache: Englisch
Verlag: Springer-Nature New York Inc, 2023
ISBN 10: 3031458567 ISBN 13: 9783031458569
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 83,87
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In den WarenkorbPaperback. Zustand: Brand New. 180 pages. 9.25x6.10x0.39 inches. In Stock.
Anbieter: Brook Bookstore, Milano, MI, Italien
Zustand: new. Caterina Ferrante (illustrator).
Sprache: Englisch
Verlag: Springer Nature Switzerland, Springer International Publishing, 2023
ISBN 10: 3031458567 ISBN 13: 9783031458569
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Taschenbuch. Zustand: Neu. Domain Adaptation and Representation Transfer | 5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings | Lisa Koch (u. a.) | Taschenbuch | Lecture Notes in Computer Science | x | Englisch | 2023 | Springer | EAN 9783031458569 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.