Mining Biomedical Text Images And Visual Features For Information Retrieval
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Mining Biomedical Text, Images and Visual Features for Information Retrieval
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. - Describes many biomedical imaging techniques to detect diseases at the cellular level i.e., image segmentation, classification, or image indexing using a variety of computational intelligence and image processing approaches - Discusses how data mining techniques can be used for noise diminution and filtering MRI, EEG, MEG, fMRI, fNIRS, and PET Images - Presents text mining techniques used for clinical documents in the areas of medicine and Biomedical NLP Systems
Biomedical Data Mining for Information Retrieval
BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
AI in Diagnostic Radiology: Clinical Applications and Case-Based Insights
AI rapidly transforms diagnostic radiology, offering powerful tools to enhance image interpretation, streamline workflows, and improve diagnostic accuracy. By utilizing deep learning algorithms trained on medical images, AI systems can detect abnormalities with precision comparable to experienced radiologists in certain contexts. These advancements have found real-world application in areas like chest X-ray analysis, mammography, CT and MRI interpretation, and triage in emergency imaging. Case-based insights demonstrate how AI assists in early disease detection, supports differential diagnosis, and reduces diagnostic errors, contributing to better patient outcomes. However, effective clinical integration requires careful validation, consideration of ethical implications, and collaboration between radiologists and AI developers to ensure technology works with, rather than replaces, human expertise. AI in Diagnostic Radiology: Clinical Applications and Case-Based Insights explores the use of AI in diagnostic radiology to enhance image analysis, improve diagnostic accuracy, and streamline clinical workflows. It explains real-world applications through case-based insights, demonstrating how AI supports radiologists in detecting and interpreting medical conditions. This book covers topics such as medical detection, deep learning, and radiology, and is a useful resource for medical professionals, computer engineers, academicians, researchers, and scientists.