Urologic Oncology / Digital Pathology

The junior working group "Urologic Oncology / Digital Pathology" under the guidance of Dr. Yuri Tolkach has research interests in following areas:

  1. Computational pathology and artificial intelligence - based methods for diagnostic and prognostic applications (including correlation to radiology and nuclear medicine imaging).
  2. Molecular characterization of the uro-oncological diseases.
  3. Genetic evolution of primary and metastatic prostate cancer, including mathematical modelling (in cooperation with Florian Kreten and Anton Bovier, Hausdorff Center for Mathematics, University of Bonn, Bonn, Germany).
Related publications

  1. Tolkach Y, et al. High-accuracy prostate cancer pathology using deep learning. Nature Machine Intelligence 2020. doi: 10.1038/s42256-020-0200-7.
  2. Eminaga O, Tolkach Y, Kunder C, et al. Deep Learning for Prostate Pathology (Preprint). ArXiv 2019. arXiv:1910.04918.
  3. Eminaga O, Abbas M, Tolkach Y, et al. Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning (Preprint). ArXiv 2019. arXiv:1910.09100.
  4. Kremer A, Kremer T, Kristiansen G, and Tolkach Y. Where is the limit of prostate cancer biomarker research? Systematic investigation of potential prognostic and diagnostic biomarkers. BMC Urology 2019; 19:46.
  5. Tolkach Y, Ellinger J, Kremer A, et al. Apelin and apelin receptor expression in renal cell carcinoma. Br J Cancer 2019; 120: 633-639.
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