Data-Driven Clinical Decision-Making Using Deep Learning in Imaging - Couverture souple

Couverture souple

ISBN 10 : 9819739683 ISBN 13 : 9789819739684

Editeur : Springer Verlag, Singapore, 2025

Synopsis

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.

Détails de l'édition

• Éditeur: Springer Verlag, Singapore
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 9819739683
• ISBN 13: 9789819739684
• Reliure: Broché
• Nombre de pages: 286
• Éditeur: Mridha M. F., Dey Nilanjan
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne