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Convolutional NeuralNetworks forRadiologic Images: ARadiologist’s Guide Shelly SofferAvi Ben-CohenOrit Shimon2019 год

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
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Страница публикацииПубликация в OpenAlex
Аннотация: Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an introduction to deep learning technology and presents the stages that are entailed in the design process of deep learning radiology research. In addition, the article details the results of a survey of the application of deep learning—specifically, the application of convolutional neural networks—to radiologic imaging that was focused on the following five major system organs: chest, breast, brain, musculoskeletal system, and abdomen and pelvis. The survey of the studies is followed by a discussion about current challenges and future trends and their potential implications for radiology. This article may be used as a guide for radiologists planning research in the field of radiologic image analysis using convolutional neural networks. © RSNA, 2019
Год издания: 2019
Авторы: Shelly Soffer, Avi Ben-Cohen, Orit Shimon, Michal Marianne Amitai, Hayit Greenspan, Eyal Klang
Издательство: Radiological Society of North America
Источник: Radiology
Ключевые слова: Radiomics and Machine Learning in Medical Imaging, COVID-19 diagnosis using AI, AI in cancer detection
Другие ссылки: Radiology (HTML)
PubMed (HTML)