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Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future.

, , , , and . Comput. Biol. Medicine, (2020)

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A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study., , , , , , , , and . J. Digit. Imaging, 29 (4): 476-487 (2016)CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation., , , , , , , , , and 7 other author(s). Bioinform., 38 (16): 4002-4010 (2022)Representation of Deep Features using Radiologist defined Semantic Features., , , , , , , and . IJCNN, page 1-7. IEEE, (2018)Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial., , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 9787 of SPIE Proceedings, page 97870H. SPIE, (2016)Stability of deep features across CT scanners and field of view using a physical phantom., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105753P. SPIE, (2018)Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic features in tumor habitats., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97850T. SPIE, (2016)Imbalanced learning for clinical survival group prediction of brain tumor patients., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 94142K. SPIE, (2015)Using features from tumor subregions of breast DCE-MRI for estrogen receptor status prediction., , , , , , and . SMC, page 2624-2629. IEEE, (2014)Improving malignancy prediction through feature selection informed by nodule size ranges in NLST., , , , , , and . SMC, page 1939-1944. IEEE, (2016)Deep radiomics: deep learning on radiomics texture images., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11597 of SPIE Proceedings, SPIE, (2021)