Analysis of sex-related differences in the diagnostic performance of a market-approved convolutional neural network for skin cancer detection
Sies et al. (2022)

Classification by a market-approved convolutional neural network in comparison with 64 dermatologists
Haenssle et al. (2021)

The use of noninvasive imaging techniques in the diagnosis of melanoma: a prospective diagnostic accuracy study
MacLellan et al. (2021)

Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition
Winkler et al. (2021)

Haenssle et al. (2020)

Classification by a market-approved convolutional neural network in comparison with 64 dermatologists.
Haenssle et al. (2020)

Performance in different melanoma subtypes and localisations.
Winkler et al. (2020)

A Prospective Diagnostic Accuracy Study
MacLellan et al. (2020)

Tschandl (2020)

Performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesion.
Sies et al. (2020)

Fink et al. (2020)

Fink et al. (2019)

Haenssle, Fink et al. (2018)