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

Dermatologist-level classification of skin cancer with deep neural networks

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2013 IEEE 10th International Symposium on Biomedical Imaging

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Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

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Human-computer collaboration for skin cancer recognition

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Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting

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Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

Han, S. S., Kim, M. S., Lim, W., Park, G., Park, I., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.01.028 · Cited by 715 · see citing works

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 · doi:10.1016/j.ejca.2019.04.001 · Cited by 605 · see citing works

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

Tschandl, P., Codella, N., Akay, B. N., Argenziano, G., Braun, R. P., Cabo, H., Gutman, D., Halpern, A., Helba, B., Hofmann-Wellenhof, R., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30333-x · Cited by 555 · see citing works

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 · doi:10.1016/j.ejca.2019.05.023 · Cited by 415 · see citing works

Melanoma detection by analysis of clinical images using convolutional neural network

Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M. R., Jafari, M. H., Ward, K., & Najarian, K. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590963 · Cited by 387 · see citing works

Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition

Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., et al. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.1735 · Cited by 365 · see citing works

Superior skin cancer classification by the combination of human and artificial intelligence

Hekler, A., Utikal, J. S., Enk, A. H., Hauschild, A., Weichenthal, M., Maron, R. C., Berking, C., Haferkamp, S., Klode, J., Schadendorf, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.07.019 · Cited by 357 · see citing works

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

Marchetti, M. A., Codella, N., Dusza, S. W., Gutman, D. A., Helba, B., Kalloo, A., Mishra, N. K., Carrera, C., Celebi, M. E., DeFazio, J., et al. (2017) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2017.08.016 · Cited by 329 · see citing works

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks

Tschandl, P., Rosendahl, C. O., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J.-Y., Kreusch, J. F., Lallas, A., et al. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.4378 · Cited by 327 · see citing works

Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis

Fujisawa, Y., Otomo, Y., Ogata, Y., Nakamura, Y., Fujita, R., Ishitsuka, Y., Watanabe, R., Okiyama, N., Ohara, K., & Fujimoto, M. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16924 · Cited by 316 · see citing works

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Brinker, T. J., Hekler, A., Enk, A. H., Klode, J., Hauschild, A., Berking, C., Schilling, B., Haferkamp, S., Schadendorf, D., Fröhling, S., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.02.005 · Cited by 304 · see citing works

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 · doi:10.1016/j.ejca.2019.06.012 · Cited by 285 · see citing works

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020) · Biomolecules · doi:10.3390/biom10081123 · Cited by 276 · see citing works

Interobserver variability on the histopathologic diagnosis of cutaneous melanoma and other pigmented skin lesions.

Corona, R., Mele, A., Amini, M., Rosa, G. D., Coppola, G., Piccardi, P., Fucci, M., Pasquini, P., & Faraggiana, T. (1996) · Journal of Clinical Oncology · doi:10.1200/jco.1996.14.4.1218 · Cited by 267 · see citing works

Fusing fine-tuned deep features for skin lesion classification

Mahbod, A., Schaefer, G., Ellinger, I., Ecker, R., Pitiot, A., & Wang, C. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.10.007 · Cited by 265 · see citing works

Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.

Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., Chae, J. B., Huh, C. H., & Na, J.-I. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.01.019 · Cited by 248 · see citing works

Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions

Haenssle, H. A., Fink, C., Toberer, F., Winkler, J. K., Stolz, W., Deinlein, T., Hofmann‐Wellenhof, R., Lallas, A., Emmert, S., Buhl, T., et al. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2019.10.013 · Cited by 237 · see citing works

Pathologist-level classification of histopathological melanoma images with deep neural networks

Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Schadendorf, D., Jansen, P., Franklin, C., Holland-Letz, T., Krahl, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.04.021 · Cited by 231 · see citing works

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 · doi:10.1016/j.ejca.2019.06.013 · Cited by 219 · see citing works

Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting

THIERS, B. H. (2009) · Yearbook of Dermatology and Dermatologic Surgery · doi:10.1016/s0093-3619(08)79154-8 · Cited by 189 · see citing works

Meta‐analysis of digital dermoscopy follow‐up of melanocytic skin lesions: a study on behalf of the International Dermoscopy Society

Salerni, G., Terán, T. E., Puig, S., Malvehy, J., Zalaudek, I., Argenziano, G., & Kittler, H. (2012) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.12032 · Cited by 178 · see citing works

38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Discordance in the histopathologic diagnosis of difficult melanocytic neoplasms in the clinical setting

Lodha, S., Saggar, S., Celebi, J. T., & Silvers, D. N. (2008) · Journal of Cutaneous Pathology · doi:10.1111/j.1600-0560.2007.00970.x · Cited by 156 · see citing works

Acral melanoma detection using a convolutional neural network for dermoscopy images

Yu, C., Yang, S., Kim, W. O., Jung, J. W., Chung, K. Y., Lee, S. W., & Oh, B. H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193321 · Cited by 130 · see citing works

Automated Dermatological Diagnosis: Hype or Reality?

Navarrete-Dechent, C., Dusza, S. W., Liopyris, K., Marghoob, A. A., Halpern, A. C., & Marchetti, M. A. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.04.040 · Cited by 129 · see citing works

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death

Kulkarni, P. M., Robinson, E. J., Pradhan, J. S., Gartrell, R. D., Rohr, B. R., Trager, M. H., Geskin, L. J., Kluger, H. M., Wong, P. F., Ács, B., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-1495 · Cited by 124 · see citing works

Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017

Marchetti, M. A., Liopyris, K., Dusza, S. W., Codella, N., Gutman, D. A., Helba, B., Kalloo, A., Halpern, A. C., Soyer, H. P., Curiel‐Lewandrowski, C. N., et al. (2019) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2019.07.016 · Cited by 106 · see citing works

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 · doi:10.1016/j.ejca.2021.02.032 · Cited by 106 · see citing works

An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma

Ács, B., Ahmed, F. S., Gupta, S., Wong, P. F., Gartrell, R. D., Pradhan, J. S., Rizk, E. M., Rothberg, B. G., Saenger, Y. M., & Rimm, D. L. (2019) · Nature Communications · doi:10.1038/s41467-019-13043-2 · Cited by 105 · see citing works

Fusing Metadata and Dermoscopy Images for Skin Disease Diagnosis

Li, W., Zhuang, J., Wang, R., Zhang, J., & Zheng, W. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098645 · Cited by 85 · see citing works

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 · doi:10.1016/j.jaad.2021.02.009 · Cited by 70 · see citing works

Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks

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Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm

Logu, F. D., Ugolini, F., Maio, V., Simi, S., Cossu, A., Massi, D., Nassini, R., & Laurino, M. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01559 · Cited by 64 · see citing works

Robustness of convolutional neural networks in recognition of pigmented skin lesions

Maron, R. C., Haggenmüller, S., Kalle, C. V., Utikal, J. S., Meier, F. E., Gellrich, F. F., Hauschild, A., French, L. E., Schlaak, M. S., Ghoreschi, K., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2020.11.020 · Cited by 64 · see citing works

Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study

Han, S. S., Moon, I. J., Kim, S. H., Na, J., Kim, M. S., Park, G., Park, I., Kim, K., Lim, W., Lee, J. H., et al. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003381 · Cited by 61 · see citing works

Skin lesions of face and scalp – Classification by a market-approved convolutional neural network in comparison with 64 dermatologists

Haenssle, H. A., Winkler, J. K., Fink, C., Toberer, F., Enk, A., Stolz, W., Deinlein, T., Hofmann‐Wellenhof, R., Kittler, H., Tschandl, P., et al. (2020) · European Journal of Cancer · doi:10.1016/j.ejca.2020.11.034 · Cited by 48 · see citing works

Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions

Brinker, T. J., Hekler, A., Enk, A. H., & Kalle, C. V. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218713 · Cited by 34 · see citing works

Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study

Maron, R. C., Hekler, A., Krieghoff‐Henning, E., Schmitt, M., Schlager, J. G., Utikal, J. S., & Brinker, T. J. (2021) · Journal of Medical Internet Research · doi:10.2196/21695 · Cited by 32 · see citing works

Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement

Navarrete‐Dechent, C., Liopyris, K., & Marchetti, M. A. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.06.040 · Cited by 29 · see citing works

European consensus-based interdisciplinary guideline for melanoma. Part 1: Diagnostics: Update 2022

Garbe, C., Amaral, T. M. S., Peris, K., Hauschild, A., Arenberger, P., Basset‐Séguin, N., Bastholt, L., Bataille, V. A., del Mármol, V., Dréno, B., et al. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.03.008 · Cited by 333 · see citing works

Artificial Intelligence in Dermatology: Challenges and Perspectives

Liopyris, K., Gregoriou, S., Dias, J., & Stratigos, A. (2022) · Dermatology and Therapy · doi:10.1007/s13555-022-00833-8 · Cited by 195 · see citing works

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Chanda, T., Hauser, K., Hobelsberger, S., Bucher, T.-C., Garcia, C. N., Wies, C., Kittler, H., Tschandl, P., Navarrete‐Dechent, C., Podlipnik, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-43095-4 · Cited by 196 · see citing works

Skin Cancer Classification With Deep Learning: A Systematic Review

Wu, Y., Chen, B., Zeng, A., Pan, D., Wang, R., & Zhao, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.893972 · Cited by 210 · see citing works

Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images

Bechelli, S., & Delhommelle, J. (2022) · Bioengineering · doi:10.3390/bioengineering9030097 · Cited by 181 · see citing works

Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images

Kaur, R., GholamHosseini, H., Sinha, R., & Lindén, M. (2022) · Sensors · doi:10.3390/s22031134 · Cited by 186 · see citing works

Explainable artificial intelligence in skin cancer recognition: A systematic review

Hauser, K., Kurz, A., Haggenmüller, S., Maron, R. C., Kalle, C. V., Utikal, J. S., Meier, F. E., Hobelsberger, S., Gellrich, F. F., Sergon, M., et al. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.02.025 · Cited by 185 · see citing works

A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis

Salinas, M. P., Sepúlveda, J., Hidalgo, L., Peirano, D., Morel-Marambio, M., Uribe, P., Rotemberg, V. M., Briones, J., Mery, D., & Navarrete‐Dechent, C. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01103-x · Cited by 165 · see citing works

State-of-the-Art in 1D Convolutional Neural Networks: A Survey

Ige, A. O., & Sibiya, M. (2024) · IEEE Access · doi:10.1109/access.2024.3433513 · Cited by 154 · see citing works

A robust deep learning framework for multiclass skin cancer classification

Özdemir, B., & Paçal, İ. (2025) · Scientific Reports · doi:10.1038/s41598-025-89230-7 · Cited by 153 · see citing works

A survey, review, and future trends of skin lesion segmentation and classification

Hasan, M. K., Ahamad, M. A., Yap, C. H., & Yang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106624 · Cited by 157 · see citing works

AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions

Melarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P. (2023) · Cancers · doi:10.3390/cancers15041183 · Cited by 144 · see citing works

Artificial Intelligence in Skin Cancer Diagnosis: A Reality Check

Brancaccio, G., Balato, A., Malvehy, J., Puig, S., Argenziano, G., & Kittler, H. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.10.004 · Cited by 137 · see citing works

An innovative deep learning framework for skin cancer detection employing ConvNeXtV2 and focal self-attention mechanisms

Özdemir, B., & Paçal, İ. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103692 · Cited by 127 · see citing works

Computational pathology: A survey review and the way forward

Hosseini, M. S., Bejnordi, B. E., Trinh, V. Q., Chan, L., Hasan, D., Li, X., Yang, S. C., Kim, T., Zhang, H., Wu, T., et al. (2024) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100357 · Cited by 124 · see citing works

An effective skin cancer classification mechanism via medical vision transformer

Aladhadh, S., Alsanea, M., Aloraini, M., Khan, T., Habib, S., & Islam, M. (2022) · Sensors · doi:10.3390/s22114008 · Cited by 113 · see citing works

A reinforcement learning model for AI-based decision support in skin cancer

Barata, C., Rotemberg, V. M., Codella, N., Tschandl, P., Rinner, C., Akay, B. N., Apalla, Z., Argenziano, G., Halpern, A. C., Lallas, A., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02475-5 · Cited by 109 · see citing works

Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study

Winkler, J. K., Blum, A., Kommoss, K. S., Enk, A., Toberer, F., Rosenberger, A., & Haenssle, H. A. (2023) · JAMA Dermatology · doi:10.1001/jamadermatol.2023.0905 · Cited by 100 · see citing works

On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives

Sterz, S., Baum, K., Biewer, S., Hermanns, H., Lauber-Rönsberg, A., Meinel, P., & Langer, M. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3659051 · Cited by 91 · see citing works

Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

Nawaz, M., Nazir, T., Masood, M., Ali, F., Khan, M. A., Tariq, U., Sahar, N., & Damaševičius, R. (2022) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22750 · Cited by 86 · see citing works

Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study)

Marchetti, M. A., Cowen, E. A., Kurtansky, N. R., Weber, J., Dauscher, M., DeFazio, J., Deng, L., Dusza, S. W., Haliasos, H. C., Halpern, A. C., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00872-1 · Cited by 85 · see citing works

Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review

Patel, R. H., Foltz, E. A., Witkowski, A. M., & Łudzik, J. (2023) · Cancers · doi:10.3390/cancers15194694 · Cited by 88 · see citing works

Developments and Clinical Applications of Noninvasive Optical Technologies for Skin Cancer Diagnosis

Owida, H. A. (2022) · Journal of Skin Cancer · doi:10.1155/2022/9218847 · Cited by 83 · see citing works

Comparison of humans versus mobile phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: a multicentre, prospective, diagnostic, clinical trial

Menzies, S. W., Sinz, C., Menzies, M., Lo, S., Yolland, W., Lingohr, J., Razmara, M., Tschandl, P., Guitera, P., Scolyer, R. A., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00130-9 · Cited by 82 · see citing works

European consensus-based interdisciplinary guideline for melanoma. Part 1: Diagnostics - Update 2024

Garbe, C., Amaral, T. M. S., Peris, K., Hauschild, A., Arenberger, P., Basset‐Séguin, N., Bastholt, L., Bataille, V. A., Brochez, L., del Mármol, V., et al. (2024) · European Journal of Cancer · doi:10.1016/j.ejca.2024.115152 · Cited by 82 · see citing works

Systematic reviews of machine learning in healthcare: a literature review

Kolasa, K., Admassu, B. Y., Hołownia-Voloskova, M. E., Kędzior, K. J., Poirrier, J., & Perni, S. (2023) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2023.2279107 · Cited by 87 · see citing works

Evaluation of Artificial Intelligence–Assisted Diagnosis of Skin Neoplasms: A Single-Center, Paralleled, Unmasked, Randomized Controlled Trial

Han, S. S., Kim, Y. J., Moon, I. J., Jung, J. M., Lee, M. Y., Lee, W. J., Won, C. H., Lee, M. W., Kim, S. H., Navarrete‐Dechent, C., et al. (2022) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2022.02.003 · Cited by 77 · see citing works

Deep learning can predict lymph node status directly from histology in colorectal cancer

Kiehl, L., Kuntz, S., Höhn, J., Jutzi, T. B., Krieghoff‐Henning, E., Kather, J. N., Holland‐Letz, T., Kopp‐Schneider, A., Chang-Claude, J. C., Brobeil, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.08.039 · Cited by 76 · see citing works

A benchmark for neural network robustness in skin cancer classification

Maron, R. C., Schlager, J. G., Haggenmüller, S., Kalle, C. V., Utikal, J. S., Meier, F. E., Gellrich, F. F., Hobelsberger, S., Hauschild, A., French, L. E., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.06.047 · Cited by 69 · see citing works

Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics

Haggenmüller, S., Schmitt, M., Krieghoff‐Henning, E., Hekler, A., Maron, R. C., Wies, C., Utikal, J. S., Meier, F. E., Hobelsberger, S., Gellrich, F. F., et al. (2024) · JAMA Dermatology · doi:10.1001/jamadermatol.2023.5550 · Cited by 70 · see citing works

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours

Brinker, T. J., Kiehl, L., Schmitt, M., Jutzi, T. B., Krieghoff‐Henning, E., Krahl, D., Kutzner, H., Gholam, P., Haferkamp, S., Klode, J., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.05.026 · Cited by 65 · see citing works

Diagnostics Using Non-Invasive Technologies in Dermatological Oncology

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Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?

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