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Medical Image Learning with Limited and Noisy Data: Second International Workshop, MILLanD 2023, Held Conjunction MICCAI Vancouver, BC, Canada, October 8, Proceedings
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Medical Image Learning with Limited and Noisy Data: Second International Workshop, MILLanD 2023, Held Conjunction MICCAI Vancouver, BC, Canada, October 8, Proceedings in Franklin, TN
Current price: $129.99
Barnes and Noble
Medical Image Learning with Limited and Noisy Data: Second International Workshop, MILLanD 2023, Held Conjunction MICCAI Vancouver, BC, Canada, October 8, Proceedings in Franklin, TN
Current price: $129.99
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Size: Paperback
This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023).
The 24 full papers presented were carefully reviewed and selected from 38 submissions.
The conference focused on
challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
The 24 full papers presented were carefully reviewed and selected from 38 submissions.
The conference focused on
challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023).
The 24 full papers presented were carefully reviewed and selected from 38 submissions.
The conference focused on
challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
The 24 full papers presented were carefully reviewed and selected from 38 submissions.
The conference focused on
challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.
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