• Home /
  • Data-Driven Clinical Decision-Making Using Deep Learning in Imaging (eBook)

Data-Driven Clinical Decision-Making Using Deep Learning in Imaging (eBook)

Artikelnummer: 978-981-9739-66-0
Einband: PDF
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
CHF 236.00
decrease increase

This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.


accessibilitysupport@springernature.com

This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.


accessibilitysupport@springernature.com
Schreiben Sie Ihre eigene Bewertung
  • Nur registrierte Benutzer können Produkte bewerten
*
*
Schlecht
Sehr gut
*
*
*
*
VerlagSpringer Nature Singapore
EinbandPDF
Erscheinungsjahr2024
Seitenangabe274 S.
AusgabekennzeichenEnglisch
AbbildungenXII, 274 p. 105 illus., 92 illus. in color.
Masse15'041 KB
PlattformPDF
ReiheStudies in Big Data; Intelligent Technologies and Robotics; Intelligent Technologies and Robotics
AutorMridha, M. F. (Hrsg.) / Dey, Nilanjan (Hrsg.)

Alle Bände der Reihe "Studies in Big Data; Intelligent Technologies and Robotics; Intelligent Technologies and Robotics (R0)"

Über den Autor M. F. (Hrsg.) Mridha

M. F. Mridha (Senior Member IEEE, Professional Member ACM) is currently working as Associate Professor in the Department of Computer Science, American International University-Bangladesh (AIUB). He also worked as Associate Professor and Chairman in the Department of Computer Science and Engineering, Bangladesh University of Business and Technology (BUBT), from 2019 to 2022 and as CSE Department Faculty Member at the University of Asia Pacific and as Graduate Head from 2012 to 2019. He is Founder and Director of Advanced Machine Intelligence Research Lab (AMIR Lab). He received his Ph.D. in the domain of AI from Jahangirnagar University in the year 2017. For more than 18 years, he has been with the master's and undergraduate students as Supervisor of their thesis work. He has authored/edited several books with Springer and published more than 280 journal and conference papers. He has served as Program Committee Member in several international conferences/workshops. Nilanjan Dey received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata, and Visiting Fellow of the University of Reading, UK. He is Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society, and Series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier, etc. He is Fellow of IETE and Senior Member of IEEE.

Weitere Titel von M. F. (Hrsg.) Mridha

Produktbewertungen
Nur registrierte Benutzer können Produkte bewerten