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Deep Learning Applications in Chest Radiography and Computed Tomography

Current State of the Art

Lee, Sang Min, MD*; Seo, Joon Beom, MD*; Yun, Jihye, PhD; Cho, Young-Hoon, MD*; Vogel-Claussen, Jens, MD; Schiebler, Mark L., MD§; Gefter, Warren B., MD; van Beek, Edwin J.R., MD; Goo, Jin Mo, MD#; Lee, Kyung Soo, MD**; Hatabu, Hiroto, MD††; Gee, James, PhD‡‡; Kim, Namkug, PhD*,†

doi: 10.1097/RTI.0000000000000387
Pulmonary/Thoracic
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Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional machine learning methods, is that it can generate appropriate models for tasks directly from the raw data, removing the need for human-led feature extraction. Medical images are particularly suited for deep learning applications. Deep learning techniques have already demonstrated high performance in the detection of diabetic retinopathy on fundoscopic images and metastatic breast cancer cells on pathologic images. In radiology, deep learning has the opportunity to provide improved accuracy of image interpretation and diagnosis. Many groups are exploring the possibility of using deep learning–based applications to solve unmet clinical needs. In chest imaging, there has been a large effort to develop and apply computer-aided detection systems for the detection of lung nodules on chest radiographs and chest computed tomography. The essential limitation to computer-aided detection is an inability to learn from new information. To overcome these deficiencies, many groups have turned to deep learning approaches with promising results. In addition to nodule detection, interstitial lung disease recognition, lesion segmentation, diagnosis and patient outcomes have been addressed by deep learning approaches. The purpose of this review article was to cover the current state of the art for deep learning approaches and its limitations, and some of the potential impact on the field of radiology, with specific reference to chest imaging.

*Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center

Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center

#Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center

**Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea

Institute of Diagnostic and Interventional Radiology, German Center for Lung Research, Hannover Medical School, Hannover, Germany

§Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI

Department of Radiology, University of Pennsylvania Perelman School of Medicine

‡‡Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA

Department of Radiology, University of Edinburgh, and Edinburgh Imaging, Queen’s Medical Research Institute, Edinburgh, Scotland, UK

††Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

Edwin J.R. van Beek: Advisory boards: Imbio, Aidence; Owner/founder: Quantitative Clinical Trials Imaging Services. The remaining authors declare no conflicts of interest.

Correspondence to: Joon Beom Seo, MD, Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, South Korea (e-mail: seojb@amc.seoul.kr).

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