Mingxia Liu – författare
Machine Learning in Medical Imaging
9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings
561 kr
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687 kr
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The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.
Machine Learning in Medical Imaging
10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings
561 kr
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708 kr
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Graph Learning in Medical Imaging
First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings
544 kr
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687 kr
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This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019.
The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging.
Machine Learning in Medical Imaging
11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
1 114 kr
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1 408 kr
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This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic.
The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.