Haferkamp, Sebastian
University Hospital Regensburg
8 papers on CoChatORCID 0000-0002-3894-8345
Most cited papers
- Verified
LDHA-Associated Lactic Acid Production Blunts Tumor Immunosurveillance by T and NK Cells
Brand, A., Singer, K., Koehl, G. E., Kolitzus, M., Schoenhammer, G., Thiel, A., Matos, C., Bruss, C., Klobuch, S., Peter, K., et al. (2016) · Cell Metabolism
Cancer, Hypoxia, and MetabolismCancer Immunotherapy and Biomarkers
Cited by 2,039
- Verified
Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma
Liu, D., Schilling, B., Liu, D., Sucker, A., Livingstone, E., Jerby-Arnon, L., Zimmer, L., Gutzmer, R., Satzger, I., Loquai, C., et al. (2019) · Nature Medicine
Cancer Immunotherapy and BiomarkersCutaneous Melanoma Detection and Management
Cited by 1,039
- Verified
Deep neural networks are superior to dermatologists in melanoma image classification
Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., Weichenthal, M., Klode, J., Schadendorf, D., Holland-Letz, T., et al. (2019) · European Journal of Cancer
Cutaneous Melanoma Detection and ManagementAI in cancer detection
Cited by 415
- Verified
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
- Verified
Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark
Brinker, T. J., Hekler, A., Hauschild, A., Berking, C., Schilling, B., Enk, A. H., Haferkamp, S., Karoglan, A., von Kalle, C., Weichenthal, M., et al. (2019) · European Journal of Cancer
Cutaneous Melanoma Detection and ManagementAI in cancer detection
Cited by 157
- Verified
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
- Verified
The relative contributions of the p53 and pRb pathways in oncogene-induced melanocyte senescence
Haferkamp, S., Tran, S. L., Becker, T. M., Scurr, L. L., Kefford, R. F., & Rizos, H. (2009) · Aging
Telomeres, Telomerase, and SenescenceCancer-related Molecular Pathways
Cited by 77
- Verified
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
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