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. 2020 Apr 6;21(5):511–525. doi: 10.3348/kjr.2019.0821

Table 1. Task-Based Classification of Potential Applications of Deep Learning Technology in Field of Thoracic Radiology.

Detection of abnormalities
 Detection of lung nodule on CXR (30,43) or chest CT (69)
Image classification
 Classification of lung nodules according to morphology (71)
 Classification of lung nodules according to likelihood of malignancy (72,73,74)
 Diagnosis of specific diseases (active tuberculosis (28,29,44), lung cancer (75,77), COPD (85), pulmonary fibrosis (81,84))
 Prediction of patient prognosis or treatment response (76,85,86)
Image segmentation
 Organ segmentation (lung (95,96), pulmonary lobes (97), airway (98))
 Lung nodule segmentation (99,100)
Image generation
 Image neutralization (108,109,110)
 Image quality improvement (image noise reduction) (114,115,116)

COPD = chronic obstructive pulmonary disease, CT = computed tomography, CXR = chest X-ray