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Book
Medical Image Learning with Limited and Noisy Data : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Authors: --- --- --- --- --- et al.
ISBN: 3031449177 3031471962 Year: 2023 Publisher: Cham : Springer Nature Switzerland : Imprint: Springer,

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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.


Digital
Medical Image Learning with Limited and Noisy Data : Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Authors: --- --- --- --- --- et al.
ISBN: 9783031449178 9783031449185 9783031471964 Year: 2023 Publisher: Cham Springer Nature, Imprint: Springer

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Book
Deep Generative Models, and Data Augmentation, Labelling, and Imperfections
Authors: --- --- --- --- --- et al.
ISBN: 9783030882105 9783030882112 9783030882099 Year: 2021 Publisher: Cham Springer International Publishing :Imprint: Springer


Book
Medical image learning with limited and noisy data: second international workshop, MILLanD 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings

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Abstract

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.


Multi
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
Authors: --- --- --- --- --- et al.
ISBN: 9783030882105 9783030882112 9783030882099 Year: 2021 Publisher: Cham Springer International Publishing

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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. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems. .

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