Machine and Deep Learning in Oncology, Medical Physics and Radiology
portes grátis
Machine and Deep Learning in Oncology, Medical Physics and Radiology
El Naqa, Issam; Murphy, Martin J.
Springer Nature Switzerland AG
02/2022
513
Dura
Inglês
9783030830465
15 a 20 dias
1105
Descrição não disponível.
Part I. Introduction.- 1. What are Machine and Deep Learning?.- 2. Computational Learning Basics.- 3. Overview of Conventional Machine Learning Methods.- 4. Overview of Deep Machine Learning Methods.- 5. Quantum Computing for Machine Learning.- 6. Performance Evaluation.- 7. Software Tools for Machine and Deep learning.- 8. Data sharing, protection and bioethics.- Part II. Machine Learning for Medical Image Analysis.- 9. Detection of Cancer Lesions from Imaging.- 10. Diagnosis of Malignant and Benign Tumours.- 11. Auto-contouring for image-guidance and treatment planning.- Part III. Machine Learning for Treatment planning & Delivery.- 12. Quality Assurance and error prediction.- 13. Knowledge-based treatment planning.- 14. Intelligent respiratory motion management.- Part IV. Machine Learning for Outcomes Modeling and Decision Support.- 15. Prediction of oncology treatment outcomes.- 16. Radiomics and radiogenomics.- 17. Modelling of Radiotherapy Response (TCP/NTCP).- 18. Smartadaptive treatment strategies.- 19. Machine learning in clinical trials.
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Machine Learning;Deep Learning;Artificial Intelligence;Medical Physics;Image Analysis;Decision Support;Outcome Modelling;Radiation Physics;Treatment Planning;Radiation Oncology;Oncology;Radiology
Part I. Introduction.- 1. What are Machine and Deep Learning?.- 2. Computational Learning Basics.- 3. Overview of Conventional Machine Learning Methods.- 4. Overview of Deep Machine Learning Methods.- 5. Quantum Computing for Machine Learning.- 6. Performance Evaluation.- 7. Software Tools for Machine and Deep learning.- 8. Data sharing, protection and bioethics.- Part II. Machine Learning for Medical Image Analysis.- 9. Detection of Cancer Lesions from Imaging.- 10. Diagnosis of Malignant and Benign Tumours.- 11. Auto-contouring for image-guidance and treatment planning.- Part III. Machine Learning for Treatment planning & Delivery.- 12. Quality Assurance and error prediction.- 13. Knowledge-based treatment planning.- 14. Intelligent respiratory motion management.- Part IV. Machine Learning for Outcomes Modeling and Decision Support.- 15. Prediction of oncology treatment outcomes.- 16. Radiomics and radiogenomics.- 17. Modelling of Radiotherapy Response (TCP/NTCP).- 18. Smartadaptive treatment strategies.- 19. Machine learning in clinical trials.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
