State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of Contributors - p. xv 8. Biographies - p. xxi 9. Acknowledgments - p. xxiii 10. Chapter 1. Computer-aided detection of abnormality in mammography using deep object detectors - p. 1 11. Chapter 2. Detection of retinal abnormalities in fundus image using CNN deep learning networks - p. 19 12. Chapter 3. A survey of deep learning-based methods for cryo-electron tomography data analysis - p. 63 13. Chapter 4. Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural network features - p. 73 14. Chapter 5. Accurate identification of renal transplant rejection: convolutional neural networks and diffusion MRI - p. 91 15. Chapter 6. Applications of the ESPNet architecture in medical imaging - p. 117 16. Chapter 7. Achievements of neural network in skin lesions classification - p. 133 17. Chapter 8. A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images - p. 153 18. Chapter 9. Computer-aided diagnosis of renal masses - p. 179 19. Chapter 10. Early identification of acute rejection for renal allografts: a machine learning approach - p. 197 20. Chapter 11. Deep learning for computer-aided diagnosis in ophthalmology: a review - p. 219 21. Chapter 12. Deep learning for ophthalmology using optical coherence tomography - p. 239 22. Chapter 13. Generative adversarial networks in medical imaging - p. 271 23. Chapter 14. Deep learning from small labeled datasets applied to medical image analysis - p. 279 24. Index - p. 293
State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of Contributors - p. xv 8. Biographies - p. xxi 9. Acknowledgments - p. xxiii 10. Chapter 1. Computer-aided detection of abnormality in mammography using deep object detectors - p. 1 11. Chapter 2. Detection of retinal abnormalities in fundus image using CNN deep learning networks - p. 19 12. Chapter 3. A survey of deep learning-based methods for cryo-electron tomography data analysis - p. 63 13. Chapter 4. Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural network features - p. 73 14. Chapter 5. Accurate identification of renal transplant rejection: convolutional neural networks and diffusion MRI - p. 91 15. Chapter 6. Applications of the ESPNet architecture in medical imaging - p. 117 16. Chapter 7. Achievements of neural network in skin lesions classification - p. 133 17. Chapter 8. A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images - p. 153 18. Chapter 9. Computer-aided diagnosis of renal masses - p. 179 19. Chapter 10. Early identification of acute rejection for renal allografts: a machine learning approach - p. 197 20. Chapter 11. Deep learning for computer-aided diagnosis in ophthalmology: a review - p. 219 21. Chapter 12. Deep learning for ophthalmology using optical coherence tomography - p. 239 22. Chapter 13. Generative adversarial networks in medical imaging - p. 271 23. Chapter 14. Deep learning from small labeled datasets applied to medical image analysis - p. 279 24. Index - p. 293
