Machine Learning in Medical Imaging 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / [electronic resource] :
edited by Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Pingkun Yan.
- 1st ed. 2021.
- XVIII, 704 p. 248 illus., 232 illus. in color. online resource.
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12966 3004-9954 ; .
- Image Processing, Computer Vision, Pattern Recognition, and Graphics, 12966 .
This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 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. *The workshop was held virtually.
9783030875893
10.1007/978-3-030-87589-3 doi
Computer vision.
Artificial intelligence.
Computer engineering.
Computer networks .
Pattern recognition systems.
Bioinformatics.
Computer Vision.
Artificial Intelligence.
Computer Engineering and Networks.
Automated Pattern Recognition.
Computational and Systems Biology.
TA1634
006.37
This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 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. *The workshop was held virtually.
9783030875893
10.1007/978-3-030-87589-3 doi
Computer vision.
Artificial intelligence.
Computer engineering.
Computer networks .
Pattern recognition systems.
Bioinformatics.
Computer Vision.
Artificial Intelligence.
Computer Engineering and Networks.
Automated Pattern Recognition.
Computational and Systems Biology.
TA1634
006.37