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.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
232
16 s/w Abbildungen, 232 farbige Abbildungen
XVIII, 704 p. 248 illus., 232 illus. in color.
ISBN-13
978-3-030-87589-3 (9783030875893)
DOI
10.1007/978-3-030-87589-3
Schweitzer Klassifikation
Contrastive Representations for Continual Learning of Fine-grained Histology Images.-
