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Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil., , , , , , , , , и . EMBC, стр. 3370-3373. IEEE, (2021)Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images., , , , , , , и . EMBC, стр. 1671-1674. IEEE, (2020)Virtual biopsy in prostate cancer: can machine learning distinguish low and high aggressive tumors on MRI?, , , , , , , , и . EMBC, стр. 3374-3377. IEEE, (2021)A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI., , , , , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 7963 из SPIE Proceedings, стр. 79633Q. SPIE, (2011)A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images., , , , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 8670 из SPIE Proceedings, стр. 86703E. SPIE, (2013)A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging., , , , , , , и . Comput. Medical Imaging Graph., (2015)A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images., , , , , , , , , и 1 other автор(ы). EMBC, стр. 8029-8032. IEEE, (2011)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)Comparison of GIS-based methodologies for the landslide susceptibility assessment., , и . GeoInformatica, 13 (3): 253-265 (2009)ChiMerge discretization method: Impact on a computer aided diagnosis system for prostate cancer in MRI., , , , , и . MeMeA, стр. 297-302. IEEE, (2015)