Deep Learning for Medical Image Analysis

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache INDICE: 1. An Introduction to Neural Networks and Deep Learning 2. Medical Image Synthesis and Reconstruction 3. Dynamic Inference using Neural Architecture Search in Medical Image Segmentation 4. Cardiac 5. Applications of artificial intelligence in cardiovascular imaging 6. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning 7. An overview of disentangled representation learning for MR images 8. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging 9. Deep Learning for Medical Image Reconstruction 10. How to conduct a high quality clinical study (title TBC) 11. CapsNet 12. Hypergraph Learning and Its Applications for Medical Image Analysis 13. Unsupervised Domain Adaptation for Medical Image Analysis 14. Reinforcement Learning 15. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI 16. Deep Learning Models for Functional Brain Mapping 17. Medical Image Registration 18. Model Genesis 19. OCTA Segmentation 20. Transformer for Medical Image Analysis

• ISBN: 978-0-323-85124-4
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 600
• Fecha Publicación: 01/07/2023
• Nº Volúmenes: 1
• Idioma: Inglés