Mining Biomedical Text, Images and Visual Features for Information Retrieval provides the reader with a broad coverage of the concepts, themes, and instrumentalities of the important and evolving area of biomedical text, images, and visual features towards information retrieval. It aims to encourage an even wider adoption of IR methods for assisting in problem-solving and to stimulate research that may lead to additional innovations in this area of research.The book discusses topics such as internet of things for health informatics; data privacy; smart healthcare; medical image processing; 3D medical images; evolutionary computing; deep learning; medical ontology; linguistic indexing; lexical analysis; and domain specific semantic categories in biomedical applications.It is a valuable resource for researchers and graduate students who are interested to learn more about data mining techniques to improve their research work.
1. Chapter 1 Introduction to IoT and health informatics - p. 3 2. Chapter 2 Wireless internet of medical things: Technology and architectural design - p. 15 3. Chapter 3 An automated human behavior analysis using AI based predictive model in health care - p. 31 4. Chapter 4 Survey on security and privacy issues in IoT healthcare - p. 45 5. Chapter 5 Smart health monitoring system to prevent complications during pregnancy using IoT and Hadoop - p. 67 6. Chapter 6 Methodical IoT-based information system in healthcare industry - p. 87 7. Chapter 7 Role of IoT in developing smart healthcare monitoring systems - p. 99 8. Chapter 8 Internet of things application to study the spontaneous quantitative drugs detoxification through biosensor - p. 119 9. Chapter 9 Supervised and unsupervised techniques for biomedical image classification - p. 153 10. Chapter 10 Image segmentation and parameterization for automatic diagnostics of medical images - p. 213 11. Chapter 11 Computational intelligence on medical imaging with artificial neural networks - p. 227 12. Chapter 12 A fine-tuned deep transfer learning model in classifying multiclass brain tumors for preclinical MRI image analysis - p. 259 13. Chapter 13 Transformer models for Topic Extraction from narratives and biomedical text analysis - p. 273 14. Chapter 14 Deep learning in medical image analysis - p. 287 15. Chapter 15 Automatic segmentation of multiple organs on CT images by using deep learning approaches - p. 297 16. Chapter 16 Deep learning approaches for cervical cancer classification and segmentation: Advances and challenges - p. 319 17. Chapter 17 Synthesis of clinical images for oral cancer detection and prediction using deep learning - p. 339 18. Chapter 18 Medical image mining using data mining techniques - p. 357 19. Chapter 19 Biomedical image characterization and radio genomics using machine learning techniques - p. 397 20. Chapter 20 Image informatics for clinical and preclinical biomedical analysis - p. 423 21. Chapter 21 Persistent homology diagram (PHD) based web service for cancer tagging of mammograms - p. 461 22. Chapter 22 Tooth segmentation on dental panoramic X-rays using Mask R-CNN - p. 481 23. Chapter 23 Medical ontology for text categorization system and its applications - p. 499 24. Chapter 24 Biomedical terminologies: Resources for information retrieval - p. 513 25. Chapter 25 Multimodal medical image retrieval system for clinical decision support system - p. 537 26. Chapter 26 Translation of biomedical terms using inferring rewriting rules - p. 567 27. Chapter 27 Lexical analysis of biomedical ontologies - p. 579 28. Chapter 28 Word sense disambiguation in biomedical applications - p. 587 29. Chapter 29 Domain specific semantic categories in biomedical applications - p. 607 30. Index - p. 635
Mining Biomedical Text, Images and Visual Features for Information Retrieval provides the reader with a broad coverage of the concepts, themes, and instrumentalities of the important and evolving area of biomedical text, images, and visual features towards information retrieval. It aims to encourage an even wider adoption of IR methods for assisting in problem-solving and to stimulate research that may lead to additional innovations in this area of research.The book discusses topics such as internet of things for health informatics; data privacy; smart healthcare; medical image processing; 3D medical images; evolutionary computing; deep learning; medical ontology; linguistic indexing; lexical analysis; and domain specific semantic categories in biomedical applications.It is a valuable resource for researchers and graduate students who are interested to learn more about data mining techniques to improve their research work.
1. Chapter 1 Introduction to IoT and health informatics - p. 3 2. Chapter 2 Wireless internet of medical things: Technology and architectural design - p. 15 3. Chapter 3 An automated human behavior analysis using AI based predictive model in health care - p. 31 4. Chapter 4 Survey on security and privacy issues in IoT healthcare - p. 45 5. Chapter 5 Smart health monitoring system to prevent complications during pregnancy using IoT and Hadoop - p. 67 6. Chapter 6 Methodical IoT-based information system in healthcare industry - p. 87 7. Chapter 7 Role of IoT in developing smart healthcare monitoring systems - p. 99 8. Chapter 8 Internet of things application to study the spontaneous quantitative drugs detoxification through biosensor - p. 119 9. Chapter 9 Supervised and unsupervised techniques for biomedical image classification - p. 153 10. Chapter 10 Image segmentation and parameterization for automatic diagnostics of medical images - p. 213 11. Chapter 11 Computational intelligence on medical imaging with artificial neural networks - p. 227 12. Chapter 12 A fine-tuned deep transfer learning model in classifying multiclass brain tumors for preclinical MRI image analysis - p. 259 13. Chapter 13 Transformer models for Topic Extraction from narratives and biomedical text analysis - p. 273 14. Chapter 14 Deep learning in medical image analysis - p. 287 15. Chapter 15 Automatic segmentation of multiple organs on CT images by using deep learning approaches - p. 297 16. Chapter 16 Deep learning approaches for cervical cancer classification and segmentation: Advances and challenges - p. 319 17. Chapter 17 Synthesis of clinical images for oral cancer detection and prediction using deep learning - p. 339 18. Chapter 18 Medical image mining using data mining techniques - p. 357 19. Chapter 19 Biomedical image characterization and radio genomics using machine learning techniques - p. 397 20. Chapter 20 Image informatics for clinical and preclinical biomedical analysis - p. 423 21. Chapter 21 Persistent homology diagram (PHD) based web service for cancer tagging of mammograms - p. 461 22. Chapter 22 Tooth segmentation on dental panoramic X-rays using Mask R-CNN - p. 481 23. Chapter 23 Medical ontology for text categorization system and its applications - p. 499 24. Chapter 24 Biomedical terminologies: Resources for information retrieval - p. 513 25. Chapter 25 Multimodal medical image retrieval system for clinical decision support system - p. 537 26. Chapter 26 Translation of biomedical terms using inferring rewriting rules - p. 567 27. Chapter 27 Lexical analysis of biomedical ontologies - p. 579 28. Chapter 28 Word sense disambiguation in biomedical applications - p. 587 29. Chapter 29 Domain specific semantic categories in biomedical applications - p. 607 30. Index - p. 635
