Japanese journal of radiology
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Review
CT-based mediastinal compartment classifications and differential diagnosis of mediastinal tumors.
Division of the mediastinum into compartments is used to help narrow down the differential diagnosis of mediastinal tumors, assess tumor growth, and plan biopsies and surgical procedures. There are several traditional mediastinal compartment classification systems based upon anatomical landmarks and lateral chest radiograph. ⋯ These CT-based classification systems are useful for more consistent and exact diagnosis of mediastinal tumors. In this article, we review these CT-based mediastinal compartment classifications in relation to the differential diagnosis of mediastinal tumors.
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Deep learning has caused a third boom of artificial intelligence and great changes of diagnostic medical imaging systems such as radiology, pathology, retinal imaging, dermatology inspection, and endoscopic diagnosis will be expected in the near future. However, various attempts and new methods of deep learning have been proposed in recent years, and their progress is extremely fast. Therefore, at the initial stage when medical artificial intelligence papers were published, the artificial intelligence technology itself may be old technology or well-known general-purpose common technology. Therefore, the author has reviewed state-of-the-art computer vision papers and presentations of 2018 using deep learning technologies, which will have future clinical potentials selected from the point of view of a radiologist such as generative adversarial network, knowledge distillation, and general image data sets for supervised learning.