Name: Ai for Brain Lesion Detection and Trauma Video Action Recognition: First Bonbid-Hie Lesion Segmentation Challenge and First Trauma Thompson Challenge,. (Lecture Notes in Computer Science, 14567) (en Inglés)
Price: 132.52 USD
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Author: Bao Rina,Grant Ellen,Kirkpatrick Andrew
ISBN: 9783031716256

portada Ai for Brain Lesion Detection and Trauma Video Action Recognition: First Bonbid-Hie Lesion Segmentation Challenge and First Trauma Thompson Challenge,. (Lecture Notes in Computer Science, 14567) (en Inglés)

Ai for Brain Lesion Detection and Trauma Video Action Recognition: First Bonbid-Hie Lesion Segmentation Challenge and First Trauma Thompson Challenge,. (Lecture Notes in Computer Science, 14567) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Bao Rina,Grant Ellen,Kirkpatrick Andrew

Idioma

Inglés

N° páginas

109

Encuadernación

Tapa Blanda

ISBN13

9783031716256

N° edición

2024th

Ai for Brain Lesion Detection and Trauma Video Action Recognition: First Bonbid-Hie Lesion Segmentation Challenge and First Trauma Thompson Challenge,. (Lecture Notes in Computer Science, 14567) (en Inglés)

Bao Rina,Grant Ellen,Kirkpatrick Andrew (Autor) · Springer · Tapa Blanda

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Reseña del libro "Ai for Brain Lesion Detection and Trauma Video Action Recognition: First Bonbid-Hie Lesion Segmentation Challenge and First Trauma Thompson Challenge,. (Lecture Notes in Computer Science, 14567) (en Inglés)"

This book constitutes the proceedings of the First BONBID-HIE Lesion Segmentation Challenge and the First Trauma Thompson Challenge, held in conjunction with MICCAI 2023, in Vancouver, BC, Canada, during October 2023. For BONBID-HIE 2023 Challenge 6 papers have been accepted out of 14 submissions. They span a broad array of approaches leveraging anatomical information about HIE, data augmentation, training strategies, model architecture, and integration with traditional machine learning methods. For the TTC 2023 Trauma Thompson Challenge 4 accepted contributions are included in this book. They deal with advancements in machine learning methods and their practical applications in addressing small and diffuse lesions in HIE segmentation.