Artificial Intelligence in Medical Imaging
Deep Learning Applications in Radiology
NeucitePress ·English
Description
Artificial Intelligence in Medical Imaging provides a comprehensive examination of how machine learning and deep learning technologies are transforming diagnostic radiology and medical image analysis. This groundbreaking text bridges the gap between computer science and clinical medicine, making complex AI concepts accessible to healthcare professionals.
The book covers fundamental machine learning principles, convolutional neural networks, and their applications across all imaging modalities including radiography, CT, MRI, ultrasound, and nuclear medicine. Each chapter presents real-world clinical applications with validated algorithms and discusses implementation challenges in healthcare settings.
Special sections address regulatory considerations, FDA approval pathways, algorithm validation methodologies, and ethical implications of AI-assisted diagnosis. The text also explores emerging applications in radiomics, image reconstruction, and workflow optimization.
Contents 6 chapters
Forthcoming chapters (6)
These chapters are in preparation. Reading and download links appear when each chapter is released.
- 1Fundamentals of Deep Learning
- 2CNN Architectures
- 3Chest X-ray Analysis
- 4CT Image Segmentation
- 5MRI Reconstruction
- 6Clinical Implementation
Collective authorship
NeucitePress Board of Editors
Bibliographic record
- TitleArtificial Intelligence in Medical Imaging
- SubtitleDeep Learning Applications in Radiology
- Collective authorNeucitePress Board of Editors
- PublisherNeucitePress
- Extent6 chapters
- LanguageEnglish
