Griewank, Klaus

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Brinker, T. J., Hekler, A., Enk, A. H., Klode, J., Hauschild, A., Berking, C., Schilling, B., Haferkamp, S., Schadendorf, D., Holland-Letz, T., et al. (2019) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 605

Genetic evolution of T-cell resistance in the course of melanoma progression

Sucker, A., Zhao, F., Real, B., Heeke, C., Bielefeld, N., Maβen, S., Horn, S., Moll, I., Maltaner, R., Horn, P. A., et al. (2014) · Clinical Cancer Research

Cancer Immunotherapy and BiomarkersImmunotherapy and Immune Responses

Cited by 171

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma

Duschner, N., Baguer, D. O., Schmidt, M., Griewank, K. G., Hadaschik, E., Hetzer, S., Wiepjes, B., Le'Clerc Arrastia, J., Jansen, P., Maass, P., et al. (2023) · JDDG: Journal der Deutschen Dermatologischen Gesellschaft

AI in cancer detectionNonmelanoma Skin Cancer Studies

Cited by 14

Deep learning based histological classification of adnex tumors

Jansen, P., Arrastia, J. L., Baguer, D. O., Schmidt, M., Landsberg, J., Wenzel, J., Emberger, M., Schadendorf, D., Hadaschik, E., Maass, P., et al. (2024) · European Journal of Cancer

AI in cancer detectionCancer and Skin Lesions

Cited by 8