Deep Learning in Radiology.
By Morgan P. McBee, Omer A. Awan, Andrew T Colucci, Comeron W. Ghobadi, N. Kadom, A. Kansagra, S. Tridandapani, W. Auffermann. 2018
451 citations recorded by the source corpus.
As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care.
Subfields: Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Deep learning in generating radiology reports: A survey (2020, unknown): “Several recent surveys of DL applications [7,8] have been published in healthcare [9], electronic health records (EHR) [10], health informatics [11], medical image analysis [12,13], medicine [14,15], and even radiology [3,6,16,17].”
- A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, background): “Given enough high-quality data (text and images), it is expected that the role of CNNs will continue to expand in medicine and quantitative imaging (75-80).”