Browsing by Author "Krahl, Dieter"

Now showing items 1-3 of 3

    • A 3-dimensional histology computer model of malignant melanoma and its implications for digital pathology 

      Kurz, Alexander; Krahl, Dieter; Kutzner, Heinz; Barnhill, Raymond; Perasole, Antonio; Fernandez Figueras, Maria Teresa; Ferrara, Gerardo; Braun, Stephan A.; Starz, Hans; Llamas-Velasco, Mar; Sven Utikal, Jochen; Fröhling, Stefan; von Kalle, Christof; Kather, Jakob Nikolas; Schneider, Lucas; Brinker, Titus J. (European Journal of Cancer, 2023)
      Background: Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are ...

    • Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists 

      Brinker, Titus J.; Schmitt, Max; Krieghoff-Henning, Eva I.; Barnhill, Raymond L.; Beltraminelli, Helmut; Braun, Stephan A.; Carr, Richard; Fernandez Figueras, Maria-Teresa; Ferrara, Gerardo; Fraitag, Sylvie; Gianotti, Raffaele; Llamas-Velasco, Mar; Müller, Cornelia S.L.; Perasole, Antonio; Requena, Luis; Sangueza, Omar P.; Santonja, Carlos; Starz, Hans; Vale, Esmeralda; Weyers, Wolfgang; Dipl-Inform, Achim Hekler; Kather, Jakob N.; Fröhling, Stefan; Krahl, Dieter; Holland-Letz, Tim; Utikal, Jochen S.; Saggini, Andrea; Kutzner, Heinz (Journal of the American Academy of Dermatology, 2021-02)
      To the Editor: Currently, pathologic melanoma classification is based on the—inevitably somewhat subjective—integration of several histologic features.1 Thus, discordance between pathologists classifying ...

    • Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts 

      Haggenmüller, Sarah; Maron, Roman C.; Hekler, Achim; Utikal, Jochen S.; Barata, Catarina; Barnhill, Raymond L.; Beltraminelli, Helmut; Berking, Carola; Betz-Stablein, Brigid; Blum, Andreas; Braun, Stephan A.; Carr, Richard; Combalia, Marc; Fernandez Figueras, Maria-Teresa; Ferrara, Gerardo; Fraitag, Sylvie; French, Lars E.; Gellrich, Frank F.; Ghoreschi, Kamran; Goebeler, Matthias; Guitera, Pascale; Haenssle, Holger A.; Haferkamp, Sebastian; Heinzerling, Lucie; Heppt, Markus V.; Hilke, Franz J.; Hobelsberger, Sarah; Krahl, Dieter; Kutzner, Heinz; Lallas, Aimilios; Liopyris, Konstantinos; Llamas-Velasco, Mar; Malvehy, Josep; Meier, Friedegund; Müller, Cornelia S.L.; Navarini, Alexander A.; Navarrete-Dechent, Cristián; Perasole, Antonio; Poch, Gabriela; Podlipnik, Sebastian; Requena, Luis; Rotemberg, Veronica M.; Saggini, Andrea; Sangueza, Omar P.; Santonja, Carlos; Schadendorf, Dirk; Schilling, Bastian; Schlaak, Max; Schlager, Justin G.; Sergon, Mildred; Sondermann, Wiebke; Soyer, H. Peter; Starz, Hans; Stolz, Wilhelm; Vale, Esmeralda; Weyers, Wolfgang; Zink, Alexander; Krieghoff-Henning, Eva I.; Kather, Jakob N.; Von Kalle, Christof; Lipka, Daniel B.; Fröhling, Stefan; Hauschild, Axel; Kittler, Harald; Brinker, Titus J. (European Journal of Cancer, 2021-10)
      Background: Multiple studies have compared the performance of artificial intelligence (AI)–based models for automated skin cancer classification to human experts, thus setting the cornerstone for a ...