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Building an ensemble system for diagnosing masses in mammograms.

, , , and . Int. J. Comput. Assist. Radiol. Surg., 7 (2): 323-329 (2012)

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Automatic extraction of informal topics from online suicidal ideation., , , , , and . BMC Bioinform., 19-S (8): 57-66 (2018)Consensus Versus Disagreement in Imaging Research: a Case Study Using the LIDC Database., , , , and . J. Digit. Imaging, 25 (3): 423-436 (2012)Mapping LIDC, RadLex™, and Lung Nodule Image Features., , , and . J. Digit. Imaging, 24 (2): 256-270 (2011)Single Organ Segmentation Filters for Multiple Organ Segmentation., , and . EMBC, page 3033-3036. IEEE, (2006)Towards Achieving Diagnostic Consensus in Medical Image Interpretation., , , and . ICDM Workshops, page 771-780. IEEE Computer Society, (2014)Predictive Data Mining for Lung Nodule Interpretation., , , and . ICDM Workshops, page 157-162. IEEE Computer Society, (2007)Machine-Sourced Segmentations vs. Expert-Sourced Segmentations for the Classification of Lung Nodules with Outlier Removal., , and . ICMLA, page 510-515. IEEE, (2014)A model for the relationship between semantic and content based similarity using LIDC., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 7624 of SPIE Proceedings, page 762431. SPIE, (2010)Identifying the optimal segmentors for mass classification in mammograms., , , and . Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 941342. SPIE, (2015)An Application of Clustering Techniques to Urban Studies., , and . DMIN, page 158-164. CSREA Press, (2010)