忘川河 发表于 2025-3-25 04:16:42
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无效 发表于 2025-3-25 11:16:33
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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 fistress-test 发表于 2025-3-25 17:02:29
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-ModGRIEF 发表于 2025-3-25 21:43:31
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肉体 发表于 2025-3-26 02:07:15
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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食品室 发表于 2025-3-26 13:22:35
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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