Can deep learning model undergo the same process as a human radiologist when determining malignancy of pulmonary nodules?

Jun Keun Choi; Ehwa Yang; Chin A. Yi; Minsu Park; Jung Won Moon; Jae-Hun Kim

doi:10.1117/12.2613254

4 April 2022 Can deep learning model undergo the same process as a human radiologist when determining malignancy of pulmonary nodules?

Jun Keun Choi, Ehwa Yang, Chin A. Yi, Minsu Park, Jung Won Moon, Jae-Hun Kim

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203339 (2022) https://doi.org/10.1117/12.2613254
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

When multiple radiologists make radiological decisions based on CT scans, inter-reader variability often exists between radiologists, and thus different conclusions can be reached from viewing an identical scan. Predicting lung nodule malignancy is a prime example of a radiological decision where inter-reader variability can exist, which may be originated from the variety of radiologic features with inter-reader variability that a radiologist can consider when predicting the malignancy of nodules (1). Radiologists predict whether the nodule on chest CT is malignant or benign by observing radiologic features. Although deep learning model can be trained using 3-dimensional images, the more accurate prediction may be achieved by extracting radiologic features. The purpose of this paper is to investigate how the deep learning model can be trained to predict malignancy with regards to extracting relevant radiologic features and to compare the extent of agreement between human readers and between human readers and deep learning model for malignancy prediction of lung nodules.

Conference Presentation

Citation Download Citation

Jun Keun Choi, Ehwa Yang, Chin A. Yi, Minsu Park, Jung Won Moon, and Jae-Hun Kim "Can deep learning model undergo the same process as a human radiologist when determining malignancy of pulmonary nodules?", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203339 (4 April 2022); https://doi.org/10.1117/12.2613254

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