Biomedical Information Technology, Second Edition, contains practical, integrated clinical applications for disease detection, diagnosis, surgery, therapy and biomedical knowledge discovery, including the latest advances in the field, such as biomedical sensors, machine intelligence, artificial intelligence, deep learning in medical imaging, neural networks, natural language processing, large-scale histopathological image analysis, virtual, augmented and mixed reality, neural interfaces, and data analytics and behavioral informatics in modern medicine. The enormous growth in the field of biotechnology necessitates the utilization of information technology for the management, flow and organization of data.
All biomedical professionals can benefit from a greater understanding of how data can be efficiently managed and utilized through data compression, modeling, processing, registration, visualization, communication and large-scale biological computing.
1. Introduction - p. xxi 2. Part One. Biomedical data technologies - p. xxii 3. Chapter one. Medical imaging - p. 3 4. Chapter Two. Biomedical sensors - p. 51 5. Chapter Three. Biological computing - p. 81 6. Chapter Four. Picture archiving and communication systems and electronic medical records for the healthcare enterprise - p. 105 7. Part Two. Artificial intelligence and big data processing in biomedicine - p. 164 8. Chapter Five. Machine learning in medical imaging - p. 167 9. Chapter Six. Health intelligence - p. 197 10. Chapter Seven. Artificial intelligence in bioinformatics: Automated methodology development for protein residue contact map prediction - p. 217 11. Chapter Eight. Deep learning in biomedical image analysis - p. 239 12. CHAPTER NINE. Automatic lesion detection with three-dimensional convolutional neural networks - p. 265 13. Chapter Ten. Biomedical image segmentation for precision radiation oncology - p. 295 14. Chapter Eleven. Content-based large-scale medical image retrieval - p. 321 15. Chapter Twelve. Diversity and novelty in biomedical information retrieval - p. 369 16. Chapter Thirteen. Toward large-scale histopathological image analysis via deep learning - p. 397 17. Chapter Fourteen. Data modeling and simulation - p. 415 18. 14.1. Introduction - p. 415 19. 14.2. Compartmental models - p. 416 20. 14.3. Model identification - p. 422 21. 14.4. Model validation - p. 441 22. 14.5. Case study - p. 442 23. 14.6. Quantification of medical images - p. 445 24. 14.7. Exercises - p. 458 25. Chapter Fifteen. Image-based biomedical data modeling and parametric imaging - p. 461 26. 15.1. Introduction - p. 461 27. 15.2. Parametric image estimation methods - p. 472 28. 15.3. Noninvasive methods - p. 492 29. 15.4. Applications of parametric imaging and kinetic modeling - p. 498 30. 15.5. Summary - p. 512 31. Chapter Sixteen. Molecular imaging in biology and pharmacology - p. 523 32. Chapter Seventeen. Biomedical image visualization and display technologies - p. 561 33. Chapter Eighteen. Biomedical image characterization and radiogenomics - p. 585 34. Part Three. Emerging technologies in biomedicine - p. 613 35. Chapter Nineteen. Medical robotics and computer-integrated interventional medicine - p. 617 36. Chapter Twenty. Virtual and augmented reality in medicine - p. 673 37. Chapter Twenty one. Sensory information feedback for neural prostheses - p. 687 38. Chapter Twenty Two. Mobile health (m-health): Evidence-based progress or scientific retrogression - p. 717 39. Chapter Twenty Three. Health and medical behavior informatics - p. 735 40. Index - p. 763
Biomedical Information Technology, Second Edition, contains practical, integrated clinical applications for disease detection, diagnosis, surgery, therapy and biomedical knowledge discovery, including the latest advances in the field, such as biomedical sensors, machine intelligence, artificial intelligence, deep learning in medical imaging, neural networks, natural language processing, large-scale histopathological image analysis, virtual, augmented and mixed reality, neural interfaces, and data analytics and behavioral informatics in modern medicine. The enormous growth in the field of biotechnology necessitates the utilization of information technology for the management, flow and organization of data.
All biomedical professionals can benefit from a greater understanding of how data can be efficiently managed and utilized through data compression, modeling, processing, registration, visualization, communication and large-scale biological computing.
1. Introduction - p. xxi 2. Part One. Biomedical data technologies - p. xxii 3. Chapter one. Medical imaging - p. 3 4. Chapter Two. Biomedical sensors - p. 51 5. Chapter Three. Biological computing - p. 81 6. Chapter Four. Picture archiving and communication systems and electronic medical records for the healthcare enterprise - p. 105 7. Part Two. Artificial intelligence and big data processing in biomedicine - p. 164 8. Chapter Five. Machine learning in medical imaging - p. 167 9. Chapter Six. Health intelligence - p. 197 10. Chapter Seven. Artificial intelligence in bioinformatics: Automated methodology development for protein residue contact map prediction - p. 217 11. Chapter Eight. Deep learning in biomedical image analysis - p. 239 12. CHAPTER NINE. Automatic lesion detection with three-dimensional convolutional neural networks - p. 265 13. Chapter Ten. Biomedical image segmentation for precision radiation oncology - p. 295 14. Chapter Eleven. Content-based large-scale medical image retrieval - p. 321 15. Chapter Twelve. Diversity and novelty in biomedical information retrieval - p. 369 16. Chapter Thirteen. Toward large-scale histopathological image analysis via deep learning - p. 397 17. Chapter Fourteen. Data modeling and simulation - p. 415 18. 14.1. Introduction - p. 415 19. 14.2. Compartmental models - p. 416 20. 14.3. Model identification - p. 422 21. 14.4. Model validation - p. 441 22. 14.5. Case study - p. 442 23. 14.6. Quantification of medical images - p. 445 24. 14.7. Exercises - p. 458 25. Chapter Fifteen. Image-based biomedical data modeling and parametric imaging - p. 461 26. 15.1. Introduction - p. 461 27. 15.2. Parametric image estimation methods - p. 472 28. 15.3. Noninvasive methods - p. 492 29. 15.4. Applications of parametric imaging and kinetic modeling - p. 498 30. 15.5. Summary - p. 512 31. Chapter Sixteen. Molecular imaging in biology and pharmacology - p. 523 32. Chapter Seventeen. Biomedical image visualization and display technologies - p. 561 33. Chapter Eighteen. Biomedical image characterization and radiogenomics - p. 585 34. Part Three. Emerging technologies in biomedicine - p. 613 35. Chapter Nineteen. Medical robotics and computer-integrated interventional medicine - p. 617 36. Chapter Twenty. Virtual and augmented reality in medicine - p. 673 37. Chapter Twenty one. Sensory information feedback for neural prostheses - p. 687 38. Chapter Twenty Two. Mobile health (m-health): Evidence-based progress or scientific retrogression - p. 717 39. Chapter Twenty Three. Health and medical behavior informatics - p. 735 40. Index - p. 763
