Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Federated Quantum Convolutional Neural Networks in Healthcare: Enhancing Privacy and Efficiency

Federated Quantum Convolutional Neural Networks in Healthcare: Enhancing Privacy and Efficiency

Kumar, Abhishek
Ramírez, Reyes Juárez
Batta, Priya

Paperback

Biology

PREORDER - Expected ship date February 1, 2027

ISBN10: 0443511365
ISBN13: 9780443511363
Publisher: Academic Press
Published: Feb 1 2027
Pages: 360
Language: English
Federated Quantum Convolutional Neural Networks in Healthcare: Enhancing Privacy and Efficiency explores the innovative intersection of quantum computing and federated learning in the context of healthcare. As medical data grows exponentially and privacy concerns intensify, this book addresses the urgent need for secure, scalable AI solutions that enable collaborative data analysis without compromising patient confidentiality. It offers a detailed examination of the theoretical foundations of quantum machine learning and federated systems, complemented by practical case studies spanning medical imaging, genomics, and disease diagnosis. The content delves into architecture design, security protocols, and optimization techniques, equipping researchers and practitioners with the tools to develop privacy-preserving AI models tailored for medical applications. The book also discusses regulatory considerations, deployment challenges, and future research directions, positioning itself as a vital resource for advancing healthcare AI. Its comprehensive approach bridges fundamental science and real-world implementation, empowering healthcare professionals, data scientists, and policy makers to harness next-generation quantum federated systems for improved patient outcomes and data security. This resource is essential for those seeking to implement cutting-edge AI technologies in healthcare, ensuring secure, efficient, and collaborative medical data analysis.

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