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Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans
Author(s) -
Farhangi M. Mehdi,
Petrick Nicholas,
Sahiner Berkman,
Frigui Hichem,
Amini Amir A.,
Pezeshk Aria
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14076
Subject(s) - false positive paradox , convolutional neural network , artificial intelligence , computer science , pattern recognition (psychology) , reduction (mathematics) , medical imaging , false positive rate , deep learning , modular design , radiology , medicine , mathematics , geometry , operating system
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 computed tomography (CT) scans. Methods In our approach, a deep network consisting of 2D CNNs first processes slices individually. The features extracted in this stage are then passed to a recurrent neural network (RNN), thereby modeling consecutive slices as a sequence of temporal data and capturing the contextual information across all three dimensions in the volume of interest. Outputs of the RNN layer are weighed before the final fully connected layer, enabling the network to scale the importance of different slices within a volume of interest in an end‐to‐end training framework. Results We validated the proposed architecture on the false positive reduction track of the lung nodule analysis (LUNA) challenge for pulmonary nodule detection in chest CT scans, and obtained competitive results compared to 3D CNNs. Our results show that the proposed approach can encode the 3D information in volumetric data effectively by achieving a sensitivity >0.8 with just 1/8 false positives per scan. Conclusions Our experimental results demonstrate the effectiveness of temporal analysis of volumetric images for the application of false positive reduction in chest CT scans and show that state‐of‐the‐art 2D architectures from the literature can be directly applied to analyzing volumetric medical data. As newer and better 2D architectures are being developed at a much faster rate compared to 3D architectures, our approach makes it easy to obtain state‐of‐the‐art performance on volumetric data using new 2D architectures.