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Record Nr. |
UNISA996668470303316 |
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Autore |
Ma Jun |
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Titolo |
Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation : MICCAI Challenge, FLARE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings / / edited by Jun Ma, Bo Wang |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (476 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 15717 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Image processing - Digital techniques |
Computer vision |
Artificial intelligence |
Computer networks |
Application software |
Education - Data processing |
Software engineering |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Artificial Intelligence |
Computer Communication Networks |
Computer and Information Systems Applications |
Computers and Education |
Software Engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This book constitutes the proceedings of the MICCAI 2024 Challenge, FLARE 2024, held in Conjunction with MICCAI 2024, in Marrakesh, Morocco, during October 2024. The 20 full papers included in this book were carefully reviewed and selected from 24 submissions. They describe the solutions the participants found for automatic abdominal organ and pan-cancer segmentation using the official training dataset released for this pupose. This challenge focuses on both organ and |
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pan-cancer segmentation, including three subtasks: Subtask 1: Pan-cancer segmentation in CT scans Subtask 2: Abdominal CT organ segmentation on laptop Subtask 3: Unsupervised domain adaptation for abdominal organ segmentation in MRI Scans. |
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