Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical…mehr
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical image detection, segmentation, registration, and computer-aided analysis.
1. An Introduction to Neural Networks and Deep Learning 2. Deep reinforcement learning in medical imaging 3. CapsNet for medical image segmentation 4.Transformer for Medical Image Analysis 5. An overview of disentangled representation learning for MR images 6. Hypergraph Learning and Its Applications for Medical Image Analysis 7. Unsupervised Domain Adaptation for Medical Image Analysis 8. Medical image synthesis and reconstruction using generative adversarial networks 9. Deep Learning for Medical Image Reconstruction 10. Dynamic inference using neural architecture search in medical image segmentation 11. Multi-modality cardiac image analysis with deep learning 12. Deep Learning-based Medical Image Registration 13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI 14. Deep Learning in Functional Brain Mapping and associated applications 15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning 16. OCTA Segmentation with limited training data using disentangled represenatation learning 17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging
1. An Introduction to Neural Networks and Deep Learning 2. Deep reinforcement learning in medical imaging 3. CapsNet for medical image segmentation 4.Transformer for Medical Image Analysis 5. An overview of disentangled representation learning for MR images 6. Hypergraph Learning and Its Applications for Medical Image Analysis 7. Unsupervised Domain Adaptation for Medical Image Analysis 8. Medical image synthesis and reconstruction using generative adversarial networks 9. Deep Learning for Medical Image Reconstruction 10. Dynamic inference using neural architecture search in medical image segmentation 11. Multi-modality cardiac image analysis with deep learning 12. Deep Learning-based Medical Image Registration 13. Data-driven learning strategies for biomarker detection and outcome prediction in Autism from task-based fMRI 14. Deep Learning in Functional Brain Mapping and associated applications 15. Detecting, Localising, and Classifying Polyps from Colonoscopy Videos Using Deep Learning 16. OCTA Segmentation with limited training data using disentangled represenatation learning 17. Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging
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