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Medical Imaging and Computer-Aided Diagnosis: Proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020) de Ruidan Su

Medical Imaging and Computer-Aided Diagnosis: Proceeding of 2020 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2020): Lecture Notes in Electrical Engineering, cartea 633

Editat de Ruidan Su, Han Liu
en Limba Engleză Hardback – 3 iul 2020
This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging.
Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human–computer interaction, databases, and performance evaluation. 
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Specificații

ISBN-13: 9789811551987
ISBN-10: 9811551987
Ilustrații: X, 244 p. 107 illus., 76 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.54 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Seria Lecture Notes in Electrical Engineering

Locul publicării:Singapore, Singapore

Cuprins

Optical and Photo-acoustic Imaging.- Image Analysis and Signal Processing.- Shape Representation and Analysis.- Image Reconstruction.- Imaging and Genomics.- Image Guided Surgery.- Image-Guided Interventions and Surgery.- Segmentation.- Pattern recognition.- Feature extraction.- Classifier design.- Machine learning including deep learning.- Radiomics.- CAD workstation design.- Human-computer interaction.

Notă biografică

Dr. Ruidan Su received his M.Sc. in Software Engineering from Northeastern University, China, in 2010, and his Ph.D. degree in Computer Application Technology from Northeastern University, China, in 2014. He is currently an Assistance Professor of Shanghai Advanced Research Institute, Chinese Academy of Sciences. His field of science is digital image processing and artificial intelligence, video system processing, machine learning, computational intelligence, software engineering, data analytics, system optimization, and multi-population genetic algorithm.
Dr. Ruidan Su is an IEEE Senior Member. He has published 22 papers in refereed journals and conference proceedings. He was the Founder & Editor-in-Chief of Journal of Computational Intelligence and Electronic Systems published by American Scientific Publisher from 2012 to 2016. He was an Associate Editor for the Journal of Granular Computing Published by Springer, an Associate Editor for the Journal of Intelligent& Fuzzy Systems published by IOS Press, and a Review Board Member for Applied Intelligence.
Dr. Ruidan Su was the Guest Editor for Multimedia Tools and Applications by Springer for Special Issue on Practical Augmented Reality (AR) Technology and its Applications, a Guest Editor for the Journal of International Journal of Hydrogen Energy, and a Proceeding Editor for the Proceeding of 2018 & 2019 International Conference on Image and Video Processing, and Artificial Intelligence (IVPAI 2018 & 2019, published by SPIE). He was a Conference Chair for 2018 & 2019 International Conference on Image and Video Processing, and Artificial Intelligence, a conference Chair for 2018 3rd International Conference on Computer, Communication and Computational Sciences, and a Conference Program Committee Member for 18th International Conference on Machine Learning and Cybernetics
Dr. Ruidan Su has been a Reviewer for several leading journals, such as Information Sciences, IEEETransactions on Cybernetics, IEEE Access, Applied Intelligence, International Journal of Pattern Recognition and Artificial Intelligence, Knowledge and Information Systems, Multimedia Tools and Application, The Journal of Supercomputing, Concurrency and Computation: Practice and Experience, and Electronic Commerce Research. 
Han Liu is currently a Research Associate in Data Science in the School of Computer Science and Informatics at Cardiff University. He has previously been a Research Associate in Computational Intelligence in the School of Computing at the University of Portsmouth. He received a B.Sc. in Computing from the University of Portsmouth in 2011, an M.Sc. in Software Engineering from the University of Southampton in 2012, and a Ph.D. in Machine Learning from the University of Portsmouth in 2015. His research interests are in artificial intelligence in general and machine learning in particular. His other related areas include sentiment analysis, pattern recognition, intelligent systems, big data, granular computing, and computational intelligence.

He has published two research monographs in Springer and over 60 papers in the areas such as data mining, machine learning, and intelligent systems. One of his papers was identified as a key scientific article contributing to scientific and engineering research excellence by the selection team at Advances in Engineering and the selection rate is less than 0.1%. He also has three papers selected, respectively, as finalists of Lotfi Zadeh Best Paper Award in the 16th, 17th, and 18th International Conference on Machine Learning and Cybernetics (ICMLC 2017, 2018 & 2019).

Textul de pe ultima copertă

This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging.
Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human–computer interaction, databases, and performance evaluation. 

Caracteristici

Offers a valuable reference guide for both students and professional researchers in related fields of medical imaging and computer-aided diagnosis (CAD) Interprets current trends in the field and makes them accessible to a broad readership Combines hot-topic discussions with in-depth analysis