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Dataset homogeneity assessment for a prostate cancer CAD system., , , , и . MeMeA, стр. 1-7. IEEE, (2016)Correlation based Feature Selection impact on the classification of breast cancer patients response to neoadjuvant chemotherapy., , , , , и . MeMeA, стр. 1-5. IEEE, (2018)MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness., , , , , , и . Comput. methods Biomech. Biomed. Eng. Imaging Vis., 4 (3-4): 130-134 (2016)A dynamic assessment tool for exploring and communicating vulnerability to floods and climate change., , и . Environ. Model. Softw., (2013)Comparison between Different Approaches for the Creation of the Training Set: How Clustering and Dimensionality Impact the Performance of a Deep Learning Model., , , , , и . BIBE, стр. 393-396. IEEE, (2023)ChiMerge discretization method: Impact on a computer aided diagnosis system for prostate cancer in MRI., , , , , и . MeMeA, стр. 297-302. IEEE, (2015)Multimodal T2w and DWI Prostate Gland Automated Registration., , , , , , , и . EMBC, стр. 4427-4430. IEEE, (2019)A fully automatic lesion detection method for DCE-MRI fat-suppressed breast images., , , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 7260 из SPIE Proceedings, стр. 726026. SPIE, (2009)A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker., , , , , , и . Comput. Methods Programs Biomed., 117 (3): 482-488 (2014)Radiomics for pretreatment prediction of pathological response to neoadjuvant therapy using magnetic resonance imaging: Influence of feature selection., , , , , и . ISBI, стр. 285-288. IEEE, (2018)