| Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories … S Bernatz, J Ackermann, P Mandel, B Kaltenbach, Y Zhdanovich, ... European radiology 30, 6757-6769, 2020 | 45 | 2020 |
| Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging S Bernatz, Y Zhdanovich, J Ackermann, I Koch, PJ Wild, DP Dos Santos, ... Scientific reports 11 (1), 14248, 2021 | 23 | 2021 |
| Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans S Mahmoudi, SS Martin, J Ackermann, Y Zhdanovich, I Koch, TJ Vogl, ... BMC medical imaging 21 (1), 123, 2021 | 6 | 2021 |
| Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology Y Zhdanovich, J Ackermann, PJ Wild, J Köllermann, K Bankov, C Döring, ... BMC bioinformatics 24 (1), 1, 2023 | 2 | 2023 |
| Статистический анализ текста и задача определения авторства: дипломная работа ЕС Жданович ММФ, Кафедра дифференциальных уравнений и системного анализа, 2018 | | 2018 |
| Prediction of clinically significant prostate cancer based on magnetic resonance images and tissue microarrays applying machine learning approaches Y Zhdanovich | | |
