SubjectArtificial Intelligence (AI)
Convolutional Neural Networks (CNN)
Deep Transfer Learning
Research Subject Categories::INTERDISCIPLINARY RESEARCH AREAS
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AbstractArtificial 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.