Browsing Ciències de la Salut by Subject "Deep learning"

Now showing items 1-4 of 4

    • 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 ...

    • Automatic SCOring of atopic dermatitis using deep learning (ASCORAD): a pilot study 

      Medela, Alfonso; Mac Carthy, Taig; Aguilar Robles, S. Andy; Chiesa-Estomba, Carlos M.; Grimalt Santacana, Ramon (JID Innovations, 2022)
      Atopic dermatitis (AD) is a chronic, itchy skin condition that affects 15–20% of children, but may occur at any age. It is estimated that 16.5 million U.S. adults (7.3%) have AD that initially began at ...

    • Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms 

      Mora, Toni; Roche, David; Rodríguez-Sánchez, Beatriz (Diabetes Research and Clinical Practice, 2023)
      Aims: Using machine learning algorithms and administrative data, we aimed to predict the risk of being diagnosed with several diabetes-related complications after one-, two- and three-year post-diabetes ...

    • 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 ...