Schmitt, Max

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

Hekler, A., Utikal, J. S., Enk, A. H., Solass, W., Schmitt, M., Klode, J., Schadendorf, D., Sondermann, W., Franklin, C., Bestvater, F., et al. (2019) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

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Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Jutzi, T. B., Krieghoff-Henning, E. I., Holland-Letz, T., Utikal, J. S., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., et al. (2020) · Frontiers in Medicine

Cutaneous Melanoma Detection and ManagementArtificial Intelligence in Healthcare and Education

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

Brinker, T. J., Schmitt, M., Krieghoff-Henning, E. I., Barnhill, R., Beltraminelli, H., Braun, S. A., Carr, R., Fernandez-Figueras, M.-T., Ferrara, G., Fraitag, S., et al. (2022) · Journal of the American Academy of Dermatology

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 70

Effects of Label Noise on Deep Learning-Based Skin Cancer Classification

Hekler, A., Kather, J. N., Krieghoff-Henning, E., Utikal, J. S., Meier, F., Gellrich, F. F., Upmeier zu Belzen, J., French, L., Schlager, J. G., Ghoreschi, K., et al. (2020) · Frontiers in Medicine

Cutaneous Melanoma Detection and ManagementAI in cancer detection

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