Data Augmentation, Labelling, and Imperfections (eBook, PDF)
Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings
Redaktion: Xue, Yuan; Liu, Yihao; Zuo, Lianrui; Chen, Chao; Chen, Chen
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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 dealin...
This LNCS conference volume constitutes the proceedings of the 3rd International Workshop on
Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International
Conference 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.
Data Augmentation, Labeling, and Imperfections (DALI 2023), held on October 12, 2023, in Vancouver, Canada, in conjunction with the 26th International
Conference 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.
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