Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans.
Journal:
Medical physics
Published Date:
Mar 18, 2020
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 computed tomography (CT) scans.