A performance comparison of convolutional neural network based anthropomorphic model observer and linear model observer for signal-known statistically detection tasks

Minah Han; Byeongjoon Kim; Jongduk Baek

doi:10.1117/12.2549487

16 March 2020 A performance comparison of convolutional neural network based anthropomorphic model observer and linear model observer for signal-known statistically detection tasks

Minah Han, Byeongjoon Kim, Jongduk Baek

Author Affiliations +

Proceedings Volume 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment; 1131612 (2020) https://doi.org/10.1117/12.2549487
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Signal-known-statistically (SKS) detection task is more relevant to the clinical tasks compared to signal-knownexactly (SKE) detection task. However, anthropomorphic model observers for SKS tasks have not been studied as much as those for SKE tasks. In this study, we compare the ability of conventional model observers (i.e., channelized Hotelling observer and nonprewhitening observer with an eye-filter) and convolutional neural network (CNN) to predict human observer performance on SKS and background-known-statistically tasks in breast cone beam CT images. For model observers, we implement 1) the model which combines the responses of each signal template and 2) two-layer CNN. We implement two-layer CNN in linear and nonlinear schemes. Nonlinear CNN contains max pooling layer and nonlinear activation function which are not contained in linear CNN. Both linear and nonlinear CNN based model observers predict the rank of human observer performance for different noise structures better than conventional model observers.

Citation Download Citation

Minah Han, Byeongjoon Kim, and Jongduk Baek "A performance comparison of convolutional neural network based anthropomorphic model observer and linear model observer for signal-known statistically detection tasks", Proc. SPIE 11316, Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 1131612 (16 March 2020); https://doi.org/10.1117/12.2549487

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