2016/09/28 by Petros-Pavlos Ypsilantis, Ypsilantis, Petros-Pavlos, Giovanni Montana +1 · 1 citation
Computer Science · Mathematics · Medicine · #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Lung Cancer Diagnosis and Treatment #Machine Learning (stat.ML) #Radiomics and Machine Learning in Medical Imaging #cs.CV #stat.ML
paper · pdf · doi:10.48550/arxiv.1609.09143
openalex publication_date 2016/09/28 · arxiv created 2016/09/30 · arxiv updated 2016/10/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Computed tomography (CT) generates a stack of cross-sectional images covering a region of the body. The visual assessment of these images for the identification of potential abnormalities is a challenging and time consuming task due to the large amount of information that needs to be processed. In this article we propose a deep artificial neural network architecture, ReCTnet, for the fully-automated detection of pulmonary nodules in CT scans. The architecture learns to distinguish nodules and normal structures at the pixel level and generates three-dimensional probability maps highlighting areas that are likely to harbour the objects of interest. Convolutional and recurrent layers are combined to learn expressive image representations exploiting the spatial dependencies across axial slices. We demonstrate that leveraging intra-slice dependencies substantially increases the sensitivity to detect pulmonary nodules without inflating the false positive rate. On the publicly available LIDC/IDRI dataset consisting of 1,018 annotated CT scans, ReCTnet reaches a detection sensitivity of 90.5% with an average of 4.5 false positives per scan. Comparisons with a competing multi-channel convolutional neural network for multi-slice segmentation and other published methodologies using the same dataset provide evidence that ReCTnet offers significant performance gains.