Название: Applications of Artificial Intelligence in Medical Imaging
Автор: Abdulhamit Subasi
Издательство: Academic Press/Elsevier
Год: 2023
Страниц: 381
Язык: английский
Формат: pdf (true)
Размер: 15.3 MB

Artificial Intelligence (AI) plays an important role in the field of medical image analysis, including computer-aided diagnosis, image-guided therapy, image registration, image segmentation, image annotation, image fusion, and retrieval of image databases. With advances in medical imaging, new imaging methods and techniques are needed in the field of medical imaging, such as cone-beam/multi-slice CT, MRI, positron emission tomography (PET)/CT, 3D ultrasound imaging, diffuse optical tomography, and electrical impedance tomography, as well as new AI algorithms/applications. To provide adequate results, single-sample evidence given by the patient’s imaging data is often not appropriate. It is usually difficult to derive analytical solutions or simple equations to describe objects such as lesions and anatomy in medical images, due to wide variations and complexity. Tasks in medical image analysis therefore require learning from examples for correct image recognition (IR) and prior knowledge. This book offers advanced or up-to-date medical image analysis methods through the use of algorithms/techniques for AI, Machine Learning (ML), and IR. A picture or image is worth a thousand words, indicating that, for example, IR may play a critical role in medical imaging and diagnostics. Data/information can be learned through AI, IR, and ML in the form of an image, that is, a collection of pixels, as it is impossible to recruit experts for Big Data.

AI tools have been employed in different areas for several years. New AI methods such as Deep Learning have assisted to uncover information which entirely altered the approach in different areas. AI has reached certain maturity as an academic subject, and there are many useful books related to this subject. Since AI is an interdisciplinary subject, it must be implemented in different ways depending on the application field. Nowadays, there is a great interest in AI applications in several disciplines. This edited book presents how AI and ML methods can be used in the medical image analysis. Different AI applications in different fields, including biomedical engineering, electrical engineering, Computer Science, information technology, medical science, and healthcare, are the applications of AI to problems in these fields.

This book provides the description of various biomedical image analyses in several disease detection using AI and can therefore be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET, and ultrasound. In this way, a more integrated and, thus, more holistic research on biomedical image analysis may contribute significantly to the successful enhancement of a single patient’s clinical knowledge.

This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis.

The author of this book has a lot of hands-on experience using Python and MATLAB to solve real-world problems in the context of the ML ecosystem. Applications of Artificial Intelligence in Medical Imaging aims to provide the readers of various skill levels with the knowledge and experience necessary to develop useful AI solutions. Additionally, this book serves as a solution manual for creating sophisticated real-world systems. This provides a structured framework with guidelines, instructions, real-world examples, and code. Additionally, this book benefits from the crucial knowledge that its readers require to comprehend and resolve a variety of ML difficulties.

Discusses new deep learning algorithms for image analysis and how they are used for medical images
Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system
Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes