
AI for Brain Lesion Detection and Trauma Video Action Recognition
First BONBID-HIE Lesion Segmentation Challenge and First Trauma Thompson Challenge, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 16 and 12, 2023, Proceedings
Herausgegeben: Bao, Rina; Grant, Ellen; Kirkpatrick, Andrew; Wachs, Juan; Ou, Yangming
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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 ...
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.
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.