Deep Learning in Radiology
A Practical Guide to AI-Powered Medical Imaging Analysis
Created by judingIn "Deep Learning in Radiology: A Practical Guide to AI-Powered Medical Imaging Analysis," readers embark on a transformative journey into the world of artificial intelligence in medical imaging. This comprehensive guide is tailored specifically for radiologists and medical professionals looking to harness the power of deep learning in their practice. The book begins by laying a solid foundation in deep learning concepts, gradually progressing to advanced techniques in CNN architecture, transfer learning, and image segmentation. Readers will gain hands-on experience with PyTorch, learn to process DICOM images efficiently, and master the art of data augmentation to improve model performance. As the chapters unfold, the book delves into crucial topics such as feature extraction, hyperparameter tuning, and GPU acceleration, providing readers with the tools to optimize their deep learning models for medical imaging applications. The author also explores cutting-edge concepts like attention mechanisms, GANs, and transformer models, offering insights into their potential applications in radiology. Throughout the book, real-world case studies and practical examples demonstrate how these techniques can be applied to various medical imaging modalities, enhancing diagnostic accuracy and efficiency. By the end, readers will have gained a comprehensive understanding of deep learning in radiology, empowering them to implement AI-powered solutions in their own practice and stay at the forefront of this rapidly evolving field.
Est. Length
176 pages (7 chapters)
Language
English
Publication date
Oct 3, 2024
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