Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists
H. A. Haenssle1*,†, C. Fink1†, R. Schneiderbauer1, F. Toberer1, T. Buhl2, A. Blum3, A. Kalloo4, A. Ben Hadj Hassen5, L. Thomas6, A. Enk1 & L. Uhlmann
1Department of Dermatology, University of Heidelberg, Heidelberg; 2Department of Dermatology, University of Go ̈ttingen, Go ̈ttingen; 3Office Based Clinic of Dermatology, Konstanz, Germany; 4Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, USA; 5Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany; 6Department of Dermatology, Lyons Cancer Research Center, Lyon 1 University, Lyon, France; 7Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
Background: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN’s diagnostic performance to larger groups of dermatologists are lacking.
Methods: Google’s Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists’ diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN’s performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge.
Results: In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±69.3%) and 71.3% (±611.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±69.6%,P¼0.19) and specificity to 75.7% (±611.7%,P<0.05). The CNN ROC curve revealed a higher specificity of 82.5% when comparedwith dermatologists in level-I (±71.3%,P <0.01) and level-II (±75.7%,P<0.01) at their sensitivities of 86.6% and 88.9%, respectively.The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79,P<0.01). The CNN scored resultsclose to the top three algorithms of the ISBI 2016 challenge.
Conclusions: For the first time we compared a CNN’s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians’ experience, they may benefit from assistance by a CNN’s image classification.
Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).
Keywords:: melanoma, melanocytic nevi, dermoscopy, deep learning convolutional neural network, computer algorithm, automated melanoma detection
Over the past few decades, melanoma has emerged as a major challenge in public health . The continuous increase in incidencerates and melanoma mortality have fueled a heightened commitment to early detection and prevention . Several meta-analyses have shown that dermoscopy significantly improves the diagnostic accuracy of the naked eye examination [3–5]. However, dermatologists and medical practitioners formally trained in different dermoscopic algorithms showed an average sensitivity for detecting melanoma of mostly<80% [6,7]. In recent years, several strategiesof automated computer image analysis have been investigated asan aide for physicians to provide a high and widely reproduciblediagnostic accuracy for melanoma screening [8–11]. Theseapproaches were limited by using ‘man-made’ dermoscopic seg-mentation criteria for the diagnosis of melanoma (e.g. multiplecolors, certain morphological structures as streaks/pseudopods, ir-regular vascular structures) . As a landmark publication,Esteva et al. reported on the training and testing of a deep learningconvolutional neural network (CNN) for imaged-based classifica-tion in 2017 . In this setting the CNN was not restricted byman-made segmentation criteria, but deconstructed digital imagesdown to the pixel level and eventually created its own diagnosticclues. As in the study reported herein, the authors utilized a pre-trained GoogleNet Inception CNN architecture  additionallytrained with more than 100 000 digital images and correspondingdisease labels.
The study was approved by the local ethics committee and carried out in accordance with the Declaration of Helsinki principles.
Details on methods pertaining to the CNN architecture and CNN training are found in supplementary Methods, available atAnnals of Oncologyonline.
We used and specifically trained a modified version of Google’s Inception v4 CNN architecture (supplementary Figure S1, available at Annals of Oncologyonline) .
We created a 300-image test-set including 20% melanomas (in situ and invasive) of all body sites and of all frequent histotypes, and 80% benign melanocytic nevi of different subtypes and body sites including the so-called ‘melanoma simulators’ (supplementary Table S1, available at Annals of Oncologyonline). As almost two-third of benign nevi were non-excised lesions validated by follow-up examinations, this dataset represented a spectrum of melanocytic lesions as typically encountered in dailyclinical routine. Images of the test-set-300 were retrieved from the high-quality validated image library of the Department of Dermatology, University of Heidelberg, Germany. Various camera/dermoscope combinations were used for image acquisition. No overlap between datasets fortraining/validation and testing was allowed.
Test-set-100 and reader study level-I and -II
Before CNN testing two experienced dermatologists prospectively selected 100 images of set-300 for an increased diagnostic difficulty (supplementary Table S2, available atAnnals of Oncologyonline). Set-100 was used for CNN testing in comparison to dermatologists in a globalreader study. Readers (n¼172) were invited via mailing lists of theInternational Dermoscopy Society, and 58 (33.7%) returned their com-pleted voting sheets. Participants indicated their level of experience indermoscopy (‘Beginner’<2 years of experience, ‘Skilled’ 2–5 years of ex-perience, ‘Expert’≥5 years of experience).
In level-I of the reader study, dermatologists were presented solely the dermoscopic image and asked to indicate their dichotomous diagnosis (melanoma, benign nevus) and their management decision (excision, short-term follow-up, send away/no action needed). After an interval of 4 weeks, the same participants indicated their diagnosis and management decision in level-II of the reader study, which included dermoscopic images supplemented by additional clinical information and close-up images of the same 100 cases.
International Symposium on Biomedical Imaging challenge dataset
We used another 100-image dataset created by the International Skin Imaging Collaboration (ISIC) melanoma project for the occasion of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. This dataset enabled the direct comparison of our CNN to the inter-nationally top-five ranked algorithms .
The primary outcome measures were sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristics (ROC) for the diagnostic classification (dichotomous) of lesions by the CNN ver-sus dermatologists during level-I or -II of the reader study. Secondary end points included the assessment of the dermatologists’ diagnostic performance in their management decisions and the differences in the diagnostic performance of dermatologists between level-I and II of the reader study. For management decisions the option of a ‘short-term follow-up’ was positively accounted for both sensitivity and specificity calculations. The mean number (percentage) of all lesions and all mela-nomas indicated for follow-up, the benign nevus excision rate (numberof excised nevi/number of all nevi), and the number needed to excise(NNE; number of excised lesions/number of excised melanomas) were calculated.
The CNN put out a ‘malignancy score’ ranging from 0 to 1 with a cutoff of>0.5 for the dichotomous classification of malignant versus benign lesions. For comparison of the CNN to dermatologists a two-sided, one-sample t-test was applied and the specificity at the level of the average dermatologist sensitivity and the ROC AUC of the CNN versus the mean ROC area of dermatologists was calculated. For dermatologists’ dichotomous predictions, area under ROC curves is equivalent to the average of sensitivity and specificity. Descriptive statistics as frequency, mean, range, and standard deviation were used. Two-sidedt-tests were used to assess differences in the dermatologists’ diagnostic performance between level-I and II of the reader study. Results were considered statistically significant at the P<0.05 level. All analyses were carried out using SPSS Version 24 (IBM, SPSS; Chicago, IL).
Dermatologists’ diagnostic accuracy
Seventeen (29.3%) out of the 58 participating dermatologists from 17 countries indicated being a ‘beginner’ in dermoscopy (<2 years of experience) while 11 (19%) and 30 (51.7%) declared to be ‘skilled’ (2–5 years of experience) or an ‘expert’ (>5 years of experience), respectively. Due to reasons of feasibility dermatologists were asked to read only test-set-100.
Diagnostic classification in reader study level-I (dermoscopy only). The mean [±standard deviation (SD)] sensitivity and spe-cificity of the 58 dermatologists for the dichotomous classifica-tion of set-100 lesions during study level-I was 86.6% (±9.3%)and 71.3% (±11.2%), respectively (Table1). This translated intoan average (6SD) ROC area of 0.79 (±0.06). Experts in dermo-scopy showed a significantly higher mean sensitivity, specificity,and ROC area than beginners [89% (±9.2%), 74.5% (±12.6%),0.82 (±0.06) versus 82.9% (±7.1%), 67.6% (±.3%), 0.75(±0.04), respectively; allP<0.02; Table1].
Management decisions in reader study level-I (dermoscopy only).. Participants were offered (i) excision, (ii) short-term follow-up, or (iii) send away/no action needed as management decisions. Inthis setting, the average (±SD) sensitivity and ROC area significantly increased to 98.8% (±2.9%, P<0.01) and 0.82 (±0.07, P¼0.03), respectively (Table1). In contrast, the specificity significantly decreased from 71.3% to 64.6% (±13.6%,P<0.01). Similar changes were observed across all levels of experience. Among all dermatologists the average (±SD) benign nevus excision rate was 35.4% (±13.6%) and the lesion follow-up rate was 33.5% (±11.7%). Dermatologists included an average number(±SD) of 1.9 (±1.6) melanomas in follow-up and attained a NNE of 2.3 (±0.6). Higher experience was associated with a significant reduction of the benign nevus excision rate, the lesion follow-up rate, and the number of melanomas under follow-up (all P>0.05). The NNE also slightly improved with experience, however, without reaching statistical significance.
Diagnostic classification in reader study level-II (dermoscopy and clinical information).. The addition of clinical information (age, sex, and body site) and close-up images improved the dermatologists’ mean (±SD) sensitivity, specificity, and ROC area to 88.9%(±9.6%,P¼0.19), 75.7% (611.7%,P<0.05), and 0.82 (±0.06,P<0.01), respectively (Table1). These changes were solely basedon significant improvements of ‘beginners’ and ‘skilled’ dermatologists, while ‘experts’ in dermoscopy showed no relevant benefit from supplemented clinical information and images.
Management decisions in reader study level-II (dermoscopy and clinical information).. When asked for their management decisions during level-II of the study, dermatologists improved their level-II results of the dichotomous classification to a mean(±SD) sensitivity, specificity, and ROC area of 98.6% (±2.8%,P<0.01), 66.7% (±12.4%,P<0.01), and 0.83 (±0.06,P¼0.76)(Table1). However, we found no significant differences between these results and management decision of study level-I. The aver-age (±SD) number of melanomas included into short-term fol-low-up dropped from 1.9 (±1.6) to 1.3 (±1.5) melanomas (P¼0.03) and the NNE remained unchanged at 2.3 benign neviexcised for the detection of one melanoma. For management decisions in study level-II a higher level of experience (‘experts’ versus ‘beginners’) was associated with a significantly bettermean (±SD) ROC area [0.84 (±0.06) versus 0.79 (±0.06), P¼0.03], whereas other parameters of management decisions instudy level-II showed no significant differences in relation to the level of experience.
CNN’s diagnostic accuracy
Boxplots in Figure 1 show the distribution of melanoma prob-ability scores for benign nevi, in situmelanomas, and invasive melanomas. When the a forementioned settings were applied to test-set-100, the sensitivity, specificity, and ROC AUC were 95%, 63.8%, and 0.86, respectively. For the larger test-set-300 includ-ing less difficult-to-diagnose lesions the sensitivity, specificity,and ROC AUC were 95%, 80%, and 0.95, respectively. Both ROC curves are depicted in Figure 2A and B.
Figure 1.The CNN’s melanoma probability scores (range 0–1) forbenign nevi (green online) in comparison toin situ(orange online)or invasive melanomas (red online) are depicted as boxplots for test-set-300 and test-set-100. Scores closer to 1 indicated a higher prob-ability of melanoma. The upper and lower bounds of boxes indicatethe 25th and 75th percentiles while the median is indicated by theline intersection the upper and lower box. Whiskers indicate the fullrange of probability scores. Statistical analyses revealed significantlydifferent melanoma probability scores when comparing benignlesions toin situor invasive melanomas (P<0.001). However, melan-oma probability scores forin situand invasive melanomas showedno significant differences (set-300P¼0.84, set-100P¼0.24).
Diagnostic accuracy of CNN versus dermatologists
We used the dermatologists’ mean sensitivity of 86.6% for the diagnostic classification in study level-I as the benchmark for comparison to the CNN (Figure2A). At this sensitivity theCNN’s specificity was higher (82.5%) than the mean specificity ofdermatologists (71.3%,P<0.01). Moreover, in level-I the CNNROC AUC (0.86) was greater than the mean ROC area of derma-tologists (0.79,P<0.01).
When dermatologists received more clinical information andimages (study level-II) their diagnostic performance improved.Using the dermatologists’ level-II mean sensitivity of 88.9% asthe operating point on the CNN ROC curve, the CNN specificitywas 82.5%, which was significantly higher than the dermatolo-gists’ mean specificity of 75.7% (P<0.01). Again, the CNN ROCAUC (0.86) was greater than the mean ROC area of dermatolo-gists (0.82,P<0.01).
CNN comparison to top-five algorithms of ISBI challenge
The head-to-head comparison of ROC curves of our CNN to theinternational top-five ranked individual algorithms of the ISBI2016 challenge  is shownin Figure3. With an ROC AUC of 0.79 the CNN presented herein was among the three top algorithmsof the ISBI 2016 challenge with almost overlaying ROC curves.
Melanoma incidence rates are rising steadily in most fair-skinnedpopulations and were predicted to further increase .Notwithstanding the different levels of training and experience ofphysicians engaged in early melanoma detection, a reproduciblehigh diagnostic accuracy would be desirable. To this end, wetrained and tested a convolutional deep learning CNN for differ-entiating dermoscopic images of melanoma and benign nevi. Forthe first time we compared the diagnostic performance of a CNNwith a large international group of 58 dermatologists from 17countries, including 30 experts with more than 5 years of dermo-scopic experience. When dermatologists were provided with der-moscopic images only (study level-I) their dichotomousclassification of lesions was significantly outperformed by theCNN. However, in a real-life clinical setting dermatologists willincorporate more clinical information into decision-making.Therefore, we investigated the effect of additional clinical infor-mation and close-up images and found a much-improved diag-nostic performance of dermatologists (study level-II). However,at their improved mean sensitivity (88.9%) dermatologists stillshowed a specificity inferior to the CNN (75.7% versus 82.5%,P<0.01). Our data clearly show that a CNN algorithm may be asuitable tool to aid physicians in melanoma detection irrespectiveof their individual level of experience and training. Of note, instudy level-I thirteen (22.4%) of 58 dermatologists showed aslightly higher diagnostic performance than the CNN.
We deliberately chose the dermatologists’ dichotomous classifi-cation of lesions in set-100 as the primary outcome measure forcomparison to the CNN. However, it may be argued that ‘manage-ment decisions’ rather than ‘diagnostic classifications’ representmore the dermatologists’ everyday task in skin cancer screenings.Besides ‘excision’ and ‘send away/no action needed’ managementdecisions implied a ‘third way’, namely the option of a short-termfollow-up examination, which was introduced and validated forsingle lesions with a higher grade of atypia (e.g. variegated tonal-ities of color, asymmetry in shape, or prominent network) that donot warrant immediate excision for a suspicion of melanoma .The statistical assessment of the follow-up option introduces somedifficulties. On the one hand short-term follow-up was shown tobe an effective measure to differentiate early melanomas from be-nign nevi by unmasking dynamic changes [17–19], on the otherhand excessive use of the follow-up ‘wild-card’ (i) may be used toconceal a lack of dermoscopic expertise, (ii) may be largely imprac-ticable in daily clinical routine, and (iii) may delay melanoma exci-sion. Therefore, we positively included the choice to follow-up alesion into sensitivity (melanomas under follow-up: ‘true pos-itives’) and specificity calculations (nevi under follow-up: ‘truenegatives’). However, we also measured details about the use of the follow-up option and found that dermatologists selected approxi-mately one-third of lesions for follow-up, while the mean absolutenumber of melanomas under follow-up was in the range of 1.3–1.9. As expected, a higher level of experience and more clinical in-formation were associated with reduced follow-up rates.Important to mention, that differences in the level of difficultyinherent to any image test-set will directly impact the diagnosticperformance of algorithms and physicians. In order to generatecomparability of different computer algorithms it is therefore ofutmost importance to include a large group of dermatologists with various levels of experience as well as to create and use opensource datasets as provided by the ISIC . In contrast toMarchetti et al.  other authors have not used ‘benchmark’image datasets, and only a few studies included a small number ofreaders for comparison with their designed computer algorithms[13,20]. Moreover, wherever possible datasets should includelesions of different anatomical sites and histotypes. As shown insupplementary Tables S1 and S2, available atAnnals of Oncologyonline, both set-100 and set-300 met these requirements in orderto create a less artificial study setting.
Figure 2.(A) ROC curve of the CNN in relation to the average (6SD) sensitivity and specificity of all dermatologists [mean: green (online) cir-cle;6SD: green (online) error bars] in set-100 (dichotomous classification, study level-I) and the dermatologists’ mean sensitivity and specifi-city in relation to their level of experience. (B) ROC curve of the CNN in set-300.
Our study shows a number of limitations that may impede abroader generalization. First, as for all reader studies, the settingfor testing the dermatologists’ diagnostic performance was artifi-cial as they did not need to fear the harsh consequences of missingmelanoma. Second, the test-sets of our study did not display thefull range of lesions (e.g. pigmented basal cell carcinoma or sebor-rheic keratosis). Third, the poor availability of validated imagesled to a shortage of melanocytic lesions from other skin types andgenetic backgrounds. Fourth, as shown in earlier reader studies,operating physicians may not follow the recommendations of aCNN they not fully trust, which may diminish the reported diag-nostic performance . Besides confirmation of our results withthe help of larger and more diverse test-sets, prospective studiesare needed that also address the acceptance of patients and physi-cians involved with screening for skin cancer.
In conclusion, the results of our study demonstrate that an ad-equately trained deep learning CNN is capable of a highly accuratediagnostic classification of dermoscopic images of melanocytic ori-gin. In conjunction with results from the reader study level-I and -IIwe could show, that the CNN’s diagnostic performance was super-ior to most but not all dermatologists. While a CNN’s architectureis difficult to set up and train, its implementation on digital dermo-scopy systems or smart phone applications may easily be deployed.Therefore, physicians of all different levels of training and experi-ence may benefit from assistance by a CNN’s image classification.
study level-II: Christina Alt, Monika Arenbergerova, RenatoBakos, Anne Baltzer, Ines Bertlich, Andreas Blum, ThereziaBokor-Billmann, Jonathan Bowling, Naira Braghiroli, RalphBraun, Kristina Buder-Bakhaya, Timo Buhl, Horacio Cabo, LeoCabrijan, Naciye Cevic, Anna Classen, David Deltgen, ChristineFink, Ivelina Georgieva, Lara-Elena Hakim-Meibodi, SusanneHanner, Franziska Hartmann, Julia Hartmann, Georg Haus, EltiHoxha, Raimonds Karls, Hiroshi Koga, Ju ̈rgen Kreusch, AimiliosLallas, Pawel Majenka, Ash Marghoob, Cesare Massone, LaliMekokishvili, Dominik Mestel, Volker Meyer, Anna Neuberger,Kari Nielsen, Margaret Oliviero, Riccardo Pampena, John Paoli,Erika Pawlik, Barbar Rao, Adriana Rendon, Teresa Russo,Ahmed Sadek, Kinga Samhaber, Roland Schneiderbauer, AnissaSchweizer, Ferdinand Toberer, Lukas Trennheuser, LyobomiraVlahova, Alexander Wald, Julia Winkler, Priscila Wo ̈lbing, IrisZalaudek. Some participants asked to remain anonymous and wealso thank these colleagues for their commitment. Moreover, wethank the International Dermoscopy Society (IDS) for providingthe mailing list that enabled the invitation of dermatologists toparticipate in the study.
Figure 3.Comparison of ROC curves of the CNN described in this study (dark green (online) line) to the top-five ranked individual algo-rithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge . ROC AUCs in descending order were as follows: ISBIteam-2: 0.7956; ISBI team-1: 0.7928; ISBI team-3: 0.7892; CNN of this study: 0.7868; ISBI team-4: 0.5460; ISBI team-5: 0.5324.
Reviewed and approved by the ethic committee of the medical faculty of the University of Heidelberg (approval number S-629/2017).
This research received no specific grant from any public, com-mercial or not-for-profit sector.
HAH received honoraria and/or travel expenses from companiesinvolved in the development of devices for skin cancer screening: Scibase AB, FotoFinder Systems GmbH, Heine OptotechnikGmbH, Magnosco GmbH. CF received travel expenses fromMagnosco GmbH. The remaining authors declared no conflictsof interest.
- Koh HK. Melanoma screening: focusing the public health journey. ArchDermatol 2007; 143(1): 101–103.
- Nikolaou V, Stratigos AJ. Emerging trends in the epidemiology of melan-oma. Br J Dermatol 2014; 170(1): 11–19.
- Vestergaard ME, Macaskill P, Holt PE, Menzies SW. Dermoscopy com-pared with naked eye examination for the diagnosis of primary melan-oma: a meta-analysis of studies performed in a clinical setting. Br JDermatol 2008; 159: 669–676.
- Bafounta ML, Beauchet A, Aegerter P, Saiag P. Is dermoscopy (epilumi-nescence microscopy) useful for the diagnosis of melanoma? Results of ameta-analysis using techniques adapted to the evaluation of diagnostictests. Arch Dermatol 2001; 137(10): 1343–1350.
- Salerni G, Teran T, Puig S et al. Meta-analysis of digital dermoscopyfollow-up of melanocytic skin lesions: a study on behalf of theInternational Dermoscopy Society. J Eur Acad Dermatol Venereol 2013;27(7): 805–814.
- Dolianitis C, Kelly J, Wolfe R, Simpson P. Comparative performance of 4dermoscopic algorithms by nonexperts for the diagnosis of melanocyticlesions. Arch Dermatol 2005; 141(8): 1008–1014.
- Carli P, Quercioli E, Sestini S et al. Pattern analysis, not simplified algo-rithms, is the most reliable method for teaching dermoscopy for melan-oma diagnosis to residents in dermatology. Br J Dermatol 2003; 148(5):981–984.
- Barata C, Celebi ME, Marques JS. Improving dermoscopy image classifica-tion using color constancy. IEEE J Biomed Health Inform 2015; 19: 1–52.
- Glaister J, Wong A, Clausi DA. Segmentation of skin lesions from digitalimages using joint statistical texture distinctiveness. IEEE Trans BiomedEng 2014; 61(4): 1220–1230.
- Garnavi R, Aldeen M, Bailey J. Computer-aided diagnosis of melanomausing border and wavelet-based texture analysis. IEEE Trans InformTechnol Biomed 2012; 16(6): 1239–1252.
- Kaya S, Bayraktar M, Kockara S et al. Abrupt skin lesion border cutoffmeasurement for malignancy detection in dermoscopy images. BMCBioinformatics 2016; 17(S13): 367.
- Pehamberger H, Steiner A, Wolff K. In vivo epiluminescence microscopyof pigmented skin lesions. I. Pattern analysis of pigmented skin lesions.J Am Acad Dermatol 1987; 17(4): 571–583.
- Esteva A, Kuprel B, Novoa RA et al. Dermatologist-level classification ofskin cancer with deep neural networks. Nature 2017; 542(7639):115–118.
- Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architec-ture for computer vision 2015. https://arxiv.org/abs/1512.00567 (9 May2018, date last accessed).
- Marchetti MA, Codella NCF, Dusza SW et al. Results of the 2016International Skin Imaging Collaboration International Symposium onBiomedical Imaging challenge: comparison of the accuracy of computeralgorithms to dermatologists for the diagnosis of melanoma from der-moscopic images. J Am Acad Dermatol 2018; 78(2): 270–277.
- Menzies SW, Gutenev A, Avramidis M et al. Short-term digital surfacemicroscopic monitoring of atypical or changing melanocytic lesions.Arch Dermatol 2001; 137(12): 1583–1589.
- Altamura D, Avramidis M, Menzies SW. Assessment of the optimalinterval for and sensitivity of short-term sequential digital dermoscopymonitoring for the diagnosis of melanoma. Arch Dermatol 2008; 144(4):502–506.
- Menzies SW, Emery J, Staples M et al. Impact of dermoscopy and short-term sequential digital dermoscopy imaging for the management of pig-mented lesions in primary care: a sequential intervention trial. Br JDermatol 2009; 161(6): 1270–1277.
- Menzies SW, Stevenson ML, Altamura D, Byth K. Variables predicting change in benign melanocytic nevi under going shortterm dermoscopic imaging. Arch Dermatol 2011; 147(6): 655–659.
- Ferris LK, Harkes JA, Gilbert B et al. Computer-aided classification of melanocytic lesions using dermoscopic images. J Am Acad Dermatol 2015; 73(5): 769–776.
- Hauschild A, Chen SC, Weichenthal M et al. To excise or not: impact of MelaFind on German dermatologists’ decisions to biopsy atypicallesions. J Dtsch Dermatol Ges 2014; 12(7): 606–614.