A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing

Angeloni, Miriam; van Doeveren, Thomas; Lindner, Sebastian; Volland, Patrick; Schmelmer, Jorina; Foersch, Sebastian; Matek, Christian; Stoehr, Robert; Geppert, Carol, I; Heers, Hendrik; Wach, Sven; Taubert, Helge; Sikic, Danijel; Wullich, Bernd; van Leenders, Geert J. L. H.; Zaburdaev, Vasily; Eckstein, Markus; Hartmann, Arndt; Boormans, Joost L.; Ferrazzi, Fulvia; Bahlinger, Veronika

A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing

Autor(en):

Angeloni, Miriam; van Doeveren, Thomas; Lindner, Sebastian; Volland, Patrick; Schmelmer, Jorina; Foersch, Sebastian; Matek, Christian; Stoehr, Robert; Geppert, Carol, I; Heers, Hendrik; Wach, Sven; Taubert, Helge; Sikic, Danijel; Wullich, Bernd; van Leenders, Geert J. L. H.; Zaburdaev, Vasily; Eckstein, Markus; Hartmann, Arndt; Boormans, Joost L.; Ferrazzi, Fulvia; Bahlinger, Veronika

Tübinger Autor(en):

Bahlinger, Veronika

Erschienen in:

Journal of Pathology Clinical Research (2024), Bd. 10 (2), Article e12369

Verlagsangabe:

Hoboken : Wiley

Sprache:

Englisch

Referenz zum Volltext:

http://dx.doi.org/10.1002/2056-4538.12369

ISSN:

2056-4538