Heppt, Markus V.

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

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

Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 267

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

Maron, R. C., Weichenthal, M., Utikal, J. S., Hekler, A., Berking, C., Hauschild, A., Enk, A. H., Haferkamp, S., Klode, J., Schadendorf, D., et al. (2019) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 219

IFN-gamma-induced PD-L1 expression in melanoma depends on p53 expression

Thiem, A., Hesbacher, S., Kneitz, H., di Primio, T., Heppt, M. V., Hermanns, H. M., Goebeler, M., Meierjohann, S., Houben, R., & Schrama, D. (2019) · Journal of Experimental & Clinical Cancer Research

Cancer Immunotherapy and BiomarkersCutaneous Melanoma Detection and Management

Cited by 153

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification

Höhn, J., Krieghoff-Henning, E., Jutzi, T. B., von Kalle, C., Utikal, J. S., Meier, F., Gellrich, F. F., Hobelsberger, S., Hauschild, A., Schlager, J. G., et al. (2021) · European Journal of Cancer

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 106

Clonality of CD4+ Blood T Cells Predicts Longer Survival With CTLA4 or PD-1 Checkpoint Inhibition in Advanced Melanoma

Arakawa, A., Vollmer, S., Tietze, J., Galinski, A., Heppt, M. V., Bürdek, M., Berking, C., & Prinz, J. C. (2019) · Frontiers in Immunology

Cancer Immunotherapy and BiomarkersT-cell and B-cell Immunology

Cited by 74

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

Cited by 58

Inhibition of histone deacetylases in melanoma—a perspective from bench to bedside

Hornig, E., Heppt, M. V., Graf, S. A., Ruzicka, T., & Berking, C. (2016) · Experimental Dermatology

Histone Deacetylase Inhibitors ResearchMelanoma and MAPK Pathways

Cited by 50

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study

Wies, C., Schneider, L., Haggenmüller, S., Bucher, T.-C., Hobelsberger, S., Heppt, M. V., Ferrara, G., Krieghoff-Henning, E. I., & Brinker, T. J. (2024) · PLOS ONE

AI in cancer detectionCutaneous Melanoma Detection and Management

Cited by 16