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Proceedings Paper

Comparison of deep learning and human observer performance for lesion detection and characterization
Author(s): Ruben De Man; Grace J. Gang; Xin Li; Ge Wang
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Paper Abstract

The detection and characterizations of abnormalities in clinical imaging is of the utmost importance for patient diagnosis and treatment. In this paper, we present a comparison of convolutional neural network (CNN) and human observer performance on a simulated lesion detection and characterization task. We apply both conventional performance metrics including accuracy and non-conventional metrics such as lift charts to perform qualitative and quantitative comparison of each type of observer. It is determined that the CNN generally outperforms the human observers, particularly at high noise levels. However, high noise correlation reduces the relative performance of the CNN, and human observer performance is comparable to CNN under these conditions. These findings extend into the field of diagnostic radiology, where the adoption of deep learning is starting to become widespread. The importance of considering the applications for which deep learning is most effective is of critical importance to this development.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 110721F (28 May 2019);
Show Author Affiliations
Ruben De Man, Stony Brook Univ. (United States)
Grace J. Gang, Johns Hopkins Univ. (United States)
Xin Li, GE Global Research (United States)
Ge Wang, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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