Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches

個数：

予約

Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches

Plawiak, Pawel (EDT)/ Prakash, Allam Jaya (EDT)/ Patro, Kiran Kumar (EDT)

ウェブストア価格 ¥35,664（本体¥32,422）
Academic Press Inc（2026/01発売）
外貨定価 US$ 170.00
ポイント 324pt

現在予約受付中です。出版後の入荷・発送となります。
（重要：表示されている発売日は予定となり、発売が延期、中止、生産限定品で商品確保ができないなどの理由により、ご注文をお取消しさせていただく場合がございます。予めご了承ください。）

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 300 p.
言語 ENG
商品コード 9780443330827

Full Description

Cutting-edge Computational Intelligence in Healthcare with Convolution and Kronecker Convolution-based Approaches focuses on the use of deep learning techniques in the field of medical imagine analysis. These advances offer promising progress in healthcare through improvements in diagnostic accuracy, efficiency in medical image interpretation, and breakthroughs in treatment planning. Divided into five sections, the book begins with foundational coverage of deep learning in medical imaging and fundamentals of Convolutional Neural Networks. Discover the role convolutions play in extracting meaningful features from images, aiding tasks such as diagnosis and segmentation. The second section takes a deep dive into Kronecker convolutions and their unique advantages, such as enhanced spatial hierarchy understanding, efficient parameter utilization, and improved adaptability to specific characteristics of medical images. Section three reviews specific applications in tumor detection, enhancing organ segmentation as well as disease classification, and section four explores real-world implementation of AI-driven diagnostic imaging, precision medicine via imaging analytics, and wearable devices and continuous health monitoring. The final section offers discussion on the unique challenges, trends, and potential future directions these innovative computational approaches have on medical image processing and advanced healthcare. In summary, this book takes an interdisciplinary approach to bridge the gap between theory and practice, fusing knowledge from the domains of medicine, computer science, and machine learning to address issues in healthcare through sophisticated image analysis techniques.

Section 1. Foundational Concepts
1. Introduction to Deep Learning in Medical Imaging
2. Fundamentals of Convolutional Neural Networks

Section 2. Advanced Techniques in Deep Learning with Kronecker Convolutions
3. Kronecker Convolutions: A Deep Dive
4. Image Processing Techniques in Healthcare

Section 3. Applications in Medical Imaging
5. Kronecker Convolutions in Tumor Detection
6. Enhancing Organ Segmentation with Deep Learning
7. Disease Classification through Advanced Neural Networks

Section 4. Real-World Implementation
8. AI-Driven Diagnostic Imaging
9. Precision Medicine through Imaging Analytics
10. Wearable Devices and Continuous Monitoring

Section 5. Future Directions and Conclusion
11. Challenges and Future Directions in Medical Image Analysis
12. Conclusion and Future Trends