Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world.

Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment.

Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.

Produktdetails

• Verlag: Springer International Publishing
• Erscheinungstermin: 9. Januar 2018
• Englisch
• ISBN-13: 9783319688435
• Artikelnr.: 52939969

Inhaltsangabe

Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease.- A Comparative Study of Modern Machine Learning Approaches for Focal Lesion Detection and Classification in Medical Images: BoVW, CNN and MTANN.- Introduction to Binary Coordinate Ascent: New Insights into Efficient Feature Subset Selection for Machine Learning.- Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography.- Detecting Mammographic Masses via Image Retrieval and Discriminative Learning.

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Multi-modality Feature Learning in Diagnoses of Alzheimer's Disease.- A Comparative Study of Modern Machine Learning Approaches for Focal Lesion Detection and Classification in Medical Images: BoVW, CNN and MTANN.- Introduction to Binary Coordinate Ascent: New Insights into Efficient Feature Subset Selection for Machine Learning.- Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography.- Detecting Mammographic Masses via Image Retrieval and Discriminative Learning.