Dajiang Zhu – författare & böcker

Dajiang Zhu, Jingwen Yan, Heng Huang, Li Shen, Paul M. Thompson, Carl-Fredrik Westin, Xavier Pennec, Sarang Joshi, Mads Nielsen, Tom Fletcher, Stanley Durrleman, Stefan Sommer - Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, Häftad

Del 11846 - Lecture Notes in Computer Science

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy

4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

AvDajiang Zhu,Jingwen Yanm. fl.

Häftad, Engelska, 2019

559 kr

Skickas inom 10-15 vardagar

This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.The 16 full papers presented at MBAI 2019 and the 7 full papers presented at MFCA 2019 were carefully reviewed and selected.The MBAI papers intend to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications.The MFCA papers are devoted to statistical and geometrical methods for modeling the variability of biological shapes. The goal is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications.

Stefan Sommer, Stanley Durrleman, Tom Fletcher, Mads Nielsen, Sarang Joshi, Xavier Pennec, Carl-Fredrik Westin, Paul M. Thompson, Li Shen, Heng Huang, Jingwen Yan, Dajiang Zhu - Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy, E-bok

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy

4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

AvStefan Sommer,Stanley Durrlemanm. fl.

E-bok

Engelska, 2019

734 kr

Läs direkt efter köp

This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 16 full papers presented at MBAI 2019 and the 7 full papers presented at MFCA 2019 were carefully reviewed and selected.

The MBAI papers intend to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications.

The MFCA papers are devoted to statistical and geometrical methods for modeling the variability of biological shapes. The goal is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications.

Sandy Engelhardt, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman, Yuan Xue - Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, Häftad

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

AvSandy Engelhardt,Ilkay Oksuzm. fl.

Häftad, Engelska, 2021

704 kr

Skickas inom 10-15 vardagar

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 rigorousstudy of medical data related to machine learning systems.

Yuan Xue, Raphael Sznitman, Hien Nguyen, Sharon Xiaolei Huang, Nicholas Heller, Anirban Mukhopadhyay, Yixuan Yuan, Dajiang Zhu, Ilkay Oksuz, Sandy Engelhardt - Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, E-bok

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

AvYuan Xue,Raphael Sznitmanm. fl.

E-bok

Engelska, 2021

870 kr

Läs direkt efter köp

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 rigorousstudy of medical data related to machine learning systems.

Del 13609 - Lecture Notes in Computer Science

Deep Generative Models

Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

AvAnirban Mukhopadhyay,Ilkay Oksuzm. fl.

Häftad, Engelska, 2022

580 kr

Skickas inom 10-15 vardagar

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. DG4MICCAI 2022 accepted 12 papers from the 15 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.

Deep Generative Models

Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

AvYixuan Yuan,Dajiang Zhum. fl.

E-bok

Engelska, 2022

742 kr

Läs direkt efter köp

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. DG4MICCAI 2022 accepted 12 papers from the 15 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.

Del 14533 - Lecture Notes in Computer Science

Deep Generative Models

Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

AvAnirban Mukhopadhyay,Ilkay Oksuzm. fl.

Häftad, Engelska, 2024

1 258 kr

Skickas inom 10-15 vardagar

This LNCS conference volume constitutes the proceedings of the third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 2023. The 23 full papers included in this volume were carefully reviewed and selected from 38 submissions.The conference presents topics ranging from methodology, causal inference, latent interpretation, generative factor analysis to applications such as mammography, vessel imaging, and surgicalVideos.

Deep Generative Models

Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

AvYixuan Yuan,Dajiang Zhum. fl.

E-bok

Engelska, 2024

1 684 kr

Läs direkt efter köp

This LNCS conference volume constitutes the proceedings of the third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 2023. The 23 full papers included in this volume were carefully reviewed and selected from 38 submissions.

The conference presents topics ranging from methodology, causal inference, latent interpretation, generative factor analysis to applications such as mammography, vessel imaging, and surgical

Videos.

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Dajiang Zhu – författare

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy

Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Deep Generative Models

Deep Generative Models

Deep Generative Models

Deep Generative Models

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