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Recurrent Attention Network for False Positive Reduction in the Detection of Pulmonary Nodules in Thoracic CT Scans.

PURPOSE
Multi-view 2-D Convolutional Neural Networks (CNNs) and 3-D 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 CT scans.


METHODS
In our approach, a deep network consisting of 2-D 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 3-D CNNs. Our results show that the proposed approach can encode the 3-D 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 2-D architectures from the literature can be directly applied to analyzing volumetric medical data. As newer and better 2-D architectures are being developed at a much faster rate compared to 3-D architectures, our approach makes it easy to obtain state-of-the-art performance on volumetric data using new 2-D architectures.