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In this study, we aimed to understand the generalizability of a convolutional neural network (CNN)-based model observer for breast tomosynthesis images with two different (i.e., 30% and 50%) volume glandular fractions (VGFs). Spiculated signal with a volume equivalent to that of a spherical signal with a diameter of 1 mm was inserted at the center to generate signal-present breast volumes. The networks were optimized through brute force search in terms of depth (i.e., 5, 10, and 15 convolutional blocks) to investigate whether there is any correlation between the detection performance, and the difference between the theoretical receptive field (TRF) size of the network and the signal size. For all cases, the optimal detection performance of the CNN-based model observer was achieved when 5 convolutional blocks (i.e., TRF size of 1.1 mm) were used. To verify whether a nonlinear framework improves the generalizability of the observer, the detection performance of the CNN-based model observer was compared to that of the Hoteling observer (HO). A total of 18 tests were conducted by applying the optimal networks (i.e., N30%, N50%, Nboth) and the Hotelling templates (i.e., HT30%, HT50%, and HTboth) to each of the three testing subsets in order to compare the generalizability between the two observers. The CNN-based model observer showed a better generalized detection performance compared to that of the HO.
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