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Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets
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@article{{QIMS}{101324}, author = {Gresser, E., Schachtner, B., Stüber, A., Solyanik, O., Schreier, A., Huber, T., Froelich, M., Magistro, G., Kretschmer, A., Stief, C., Ricke, J., Ingrisch, M., Nörenberg, D.}, title = {Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets}, journal = {Quantitative Imaging in Medicine and Surgery}, volume = {12}, number = {11}, year = {2022}, url = {https://qims.amegroups.org/article/view/101324} }