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The Lung Image Database Consortium (LIDC): a quality assurance model for the collection of expert-defined truth in lung-nodule-based image analysis studies.

, , , , , , , , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 6514 of SPIE Proceedings, page 651429. SPIE, (2007)

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An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification., , , , and . CoRR, (2018)TimeLine: Visualizing Integrated Patient Records., , and . IEEE Trans. Information Technology in Biomedicine, 11 (4): 462-473 (2007)Differentiating solitary pulmonary nodules (SPNs) with 3D shape features., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 6514 of SPIE Proceedings, page 65143D. SPIE, (2007)EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)TimeLine: A Multimedia, Problem-Centric Visualization of Patient Records., , , , , , and . AMIA, AMIA, (1999)Computer-aided lung nodule diagnosis using a simple classifier., , , , , , , and . CARS, volume 1268 of International Congress Series, page 952-955. Elsevier, (2004)A concept-based retrieval system for thoracic radiology., , , , , , , , and . J. Digital Imaging, 9 (1): 25-36 (1996)An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification., , , , and . Expert Syst. Appl., (2019)The influence of CT dose and reconstruction parameters on automated detection of small pulmonary nodules., , , , , , , , and . Medical Imaging: Image Processing, volume 6144 of SPIE Proceedings, page 61445W. SPIE, (2006)Explainable Hierarchical Semantic Convolutional Neural Network for Lung Cancer Diagnosis., , , , and . CVPR Workshops, page 63-66. Computer Vision Foundation / IEEE, (2019)