Recurrent attention network for false positive reduction in the detection of pulmonary nodules in thoracic CT scans
Medical Physics2020Vol. 47(5), pp. 2150–2160
Citations Over TimeTop 12% of 2020 papers
Mohammad Mehdi Farhangi, Nicholas Petrick, Berkman Sahiner, Hichem Frigui, Amir A. Amini, Aria Pezeshk
Abstract
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.
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