Titlebook: Machine and Deep Learning in Oncology, Medical Physics and Radiology; Issam El Naqa,Martin J. Murphy Book 2022Latest edition Springer Natu

忘川河

Computerized Detection of Lesions in Diagnostic Images with Early Deep Learning Modelser of medical images are produced which physicians/radiologists must read. They may overlook lesions from such a large number of medical images. Consequently, CADe that provides suspicious lesions with radiologists/physicians is developed and becoming indispensable in their decision-making to preven

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Concerto

Auto-contouring for Image-Guidance and Treatment Planningentation of targets and normal tissues has been growing in clinical use as it can mitigate the inter- and intra-observer differences of manual segmentation and significantly reduce contouring time. Auto-segmentation has gone through advances over the years as computer technology has improved. The fi

stress-test

Machine Learning Applications in Quality Assurance of Radiation Deliveryremains within the realm of research applications, a direct connection with clinical workflows is established whenever possible. The chapter begins with a general discussion of the application of ML to QA, before diving into the analysis of Automatic Chart Review, Linac QA, and Virtual Intensity-Mod

GRIEF

Knowledge-Based Treatment Planningand critically important technology for cancer treatment. IMRT treatments rely heavily on planning expertise due to its technical complexity and the conflicting nature of maximizing tumor control while minimizing normal organ damage. As treatment planning experience and especially the carefully desi

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漫不经心

硬化

What Are Machine and Deep Learning?cians in their pursuit to realize precision medicine. This includes but is not limited to applications in computer-aided detection, classification, and diagnosis in radiology and auto-contouring, treatment planning, response modeling (radiomics, radiogenomics), image-guidance, motion tracking, and q

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拖债

Auto-contouring for Image-Guidance and Treatment Plannings. There are many different deep learning techniques, with convolutional neural networks being the most commonly used technique for segmentation tasks. Before implementation in clinics, careful QA must be carried out for auto-segmentation tasks, such as comparison with clinically approved manual con