2020
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Abstract: Purpose: Multiview two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs have been successfully used for analyzing volumetric data in many state-of-the-art medical imaging applications. We propose an alternative modular framework that analyzes volumetric data with an approach that is analogous to radiologists' interpretation, and apply the framework to reduce false positives that are generated in computer-aided detection (CADe) systems for pulmonary nodules in thoracic compu… Show more

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“…The comparison is reported in terms of sensitivities at seven different operating points and summarized at the last row using the CPM metric. The CAD system developed by Setio et al 5 used multiple-view 2D CNNs, our method and Farhangi et al 27 are based on methods of temporal analysis of CT slices, and 3D CNNs were used in rest of studies 11,13,18,19,21 in this table. It is observed that our method achieves the highest sensitivity at an average of 8 FPs/scan, but it is not superior to be best algorithms at the lowest FP rates.…”
Section: Discussionmentioning
“…The CAD system developed by Setio et al 5 . used multiple‐view 2D CNNs, our method and Farhangi et al 27 . are based on methods of temporal analysis of CT slices, and 3D CNNs were used in rest of studies 11,13,18,19,21 in this table.…”
Section: Discussionmentioning
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