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Change descriptors for determining nodule malignancy in national lung screening trial CT screening images., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 978535. SPIE, (2016)A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets., , , , , , , , , and 1 other author(s). Nat. Mach. Intell., 2 (5): 274-282 (2020)Optimal linear granulometric estimation for random sets., and . Pattern Recognit., 35 (6): 1315-1325 (2002)Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge., , , , , , , , , and 20 other author(s). IEEE Trans. Medical Imaging, 40 (12): 3748-3761 (2021)Quantitative imaging features to predict cancer status in lung nodules., , , , , , , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 9787 of SPIE Proceedings, page 97870L. SPIE, (2016)Noise factor analysis for cDNA microarrays, , , , , , , and . J Biomed Opt, 9 (4): 663-678 (July 2004)Improving malignancy prediction through feature selection informed by nodule size ranges in NLST., , , , , , and . SMC, page 1939-1944. IEEE, (2016)Clustering granulometric features., , , and . Image Processing: Algorithms and Systems, volume 4667 of SPIE Proceedings, page 36-42. SPIE, (2002)Granulometric classifiers from small samples., , , , and . Image Processing: Algorithms and Systems, volume 4667 of SPIE Proceedings, page 100-107. SPIE, (2002)Granulometric parametric estimation for the random Boolean model using optimal linear filters and optimal structuring elements., and . Pattern Recognit. Lett., 24 (1-3): 283-293 (2003)