Yuan Xue - Böcker
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3 produkter
3 produkter
Del 13003 - Lecture Notes in Computer Science
Deep Generative Models, and Data Augmentation, Labelling, and Imperfections
First Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
Häftad, Engelska, 2021
699 kr
Skickas
This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021.
Del 13567 - Lecture Notes in Computer Science
Data Augmentation, Labelling, and Imperfections
Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Häftad, Engelska, 2022
611 kr
Skickas inom 10-15 vardagar
This book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022.DALI 2022 accepted 12 papers from the 22 submissions that were reviewed.
Del 14379 - Lecture Notes in Computer Science
Data Augmentation, Labelling, and Imperfections
Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings
Häftad, Engelska, 2024
622 kr
Skickas inom 5-8 vardagar
This LNCS conference volume constitutes the proceedings of the 3rd International Workshop onData Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th InternationalConference on Medical Image Computing and Computer Assisted Intervention(MICCAI 2023). The 16 full papers together in this volume were carefully reviewed and selected from 23 submissions.The conference fosters a collaborative environment for addressing the critical challenges associated with medical data, particularly focusing on data, labeling, and dealing with data imperfections in the context of medical image analysis.