Deep Learning for Biomedical Image Reconstruction

Jong Chul Ye

ISBN 10: 1316517519 / ISBN 13: 9781316517512

Editore: Cambridge University Press Dez 2023, 2023

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Neuware - Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. Including interdisciplinary examples and a step by step background of deep learning, this book provides insight into the future of biomedical image reconstruction with clinical studies and mathematical theory.

Codice articolo 9781316517512

Dati bibliografici

• Titolo: Deep Learning for Biomedical Image Reconstruction
• Editore: Cambridge University Press Dez 2023
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1316517519
• ISBN 13: 9781316517512
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 246 L x 170 A x 24 P (millimetri) 246 millimetri di larghezza per 170 millimetri di altezza per 24 millimetri di profondità
• Peso: 885 grammi

Descrizione articolo

Riassunto

Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications.

Informazioni sugli autori

Jong Chul Ye is a Professor in the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), Korea. He is currently an associate editor for IEEE Trans. on Medical Imaging, and a Senior Editor of IEEE Signal Processing Magazine. He is an IEEE Fellow, and was the Chair of IEEE SPS Computational Imaging TC, and IEEE EMBS Distinguished Lecturer. He is the author of Geometry of Deep Learning: A Signal Processing Perspective (Springer 2022).

Yonina C. Eldar is a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel, where she heads the Center for Biomedical Engineering. She is also a Visiting Professor at MIT, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow, and the recipient of the Technical Achievement Award of the IEEE Signal Processing Society. She is author of Sampling Theory (Cambridge, 2015), and co-editor of Convex Optimization in Signal Processing and Communications (Cambridge, 2009), Compressed Sensing (Cambridge, 2012), Information-Theoretic Methods in Data Science (Cambridge 2021), and Machine Learning in Wireless Communications (Cambridge, 2022).

Michael Unser is Professor in the Institute of Electrical and Micro Engineering, EPFL, Switzerland, where he also heads the Center for Imaging. He is a Fellow of the IEEE, an elected member of the Swiss Academy of Engineering Sciences, and a EURASIP Fellow. He is recipient of the 2008 Technical Achievement Award of the IEEE Signal Processing Society and the 2020 Academic Career Achievement Award from the IEEE Engineering in Medicine and Biology Society. He is co-author of An Introduction to Sparse Stochastic Processes (Cambridge 2014).