Automated diagnosis

From dermoscopedia

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 Editor: Philipp Tschandl

 Author(s): Philipp Tschandl
Description This page gives a brief introduction and overview on neural networkThis glossary term has not yet been described. based automated diagnosisis the identification of the nature and cause of a certain phenomenon. Diagnosis is used in many different disciplines with variations in the use of logic, analytics, and experience to determine "cause and effect". In systems engineering and computer science, it is typically used to determine the causes of symptoms, mitigations, and solutions of skin lesions with dermatoscopyThe examination of [skin lesions] with a 'dermatoscope'. This traditionally consists of a magnifier (typically x10), a non-polarised light source, a transparent plate and a liquid medium between the instrument and the skin, and allows inspection of skin lesions unobstructed by skin surface reflections. Modern dermatoscopes dispense with the use of liquid medium and instead use polarised light to cancel out skin surface reflections..
Author(s) Philipp Tschandl
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Status unknown
Status update June 24, 2018
Status by Ralph P. Braun


Neural networksThis glossary term has not yet been described.

Machine learning techniques, and neural networksThis glossary term has not yet been described. in particular, for diagnosing melanomaThis glossary term has not yet been described. have been in focus of research for more than two decades in dermatology[1][2]. A systematic review as early as 2009 has shown that automated classifiers can have a comparable diagnostic odds ratio to physicians[3]. Systems reviewed in this case commonly relied on manual or semi-automatic feature extraction of digital imagesA representation of a person, animal or thing, photographed, painted or otherwise made visible.[4]. Unprecedented image classificationis a general process related to categorization, the process in which ideas and objects are recognized, differentiated, and understood. accuracy through so-called deep convolutional neural networks (CNN), which don't need a feature extraction step, has regained interest in automated analyses in the medical domain.

Comparison to human accuracy

Several studies found human level performance of such state-of-the-art CNNs to dermatologists for melanoma diagnosis[5][6][7][8][9] or multi-class accuracy[6]. Similar rates were found for classificatin of close-up images[5][10][11].

The most current endeavour is comparing multi-class accuracy of the ISIC 2018 challenge winners to human readers in a world wide and open online game.


CNNs need a large number of images during training, are thus highly dependant on datasets providing those - and are currently limited to detecting diagnoses covered in them. At the moment the largest publicly available datasets are:

The future central hub for any public dataset is probably the ISIC-Archive, where already datasets from Memorial Sloan Kettering Cancer Center, the SONIC study[12] and othersThis glossary term has not yet been described.This glossary term has not yet been described. are available in a structured way.


Although published accuracy rates for neural network classifiers - similar to studies >10 years ago - seem promising, researchers are raising concerns whether neural network classification can live up to its current promise to "solve" automated melanoma recognition[13][14]. This is due to many reasons, some exemplarily shown below:

  • Instability: Current neural networks are highly sensitive to small image perturbations invisible to the human eye, shown by adversarial attacks against medical images. But also more evident differences in images can have an impact: E.g. if only melanoma images in the training dataset are photographed with skin markings or rulers, a network may also "diagnose" images of angiomas as melanomas if depicted alongside rulers and skin markings[14].
  • Explainability: Although every parameter of a neural network is known, explainability of those systems is impeded by the vast number of parameters. One approach for intuitively explaining neural network decisions is by simply providing similar images with "content based image retrieval" (CBIR). A free example application provided by the ViDIR Group (Medical University of Vienna) can be used at
  • Evaluation: Classic evaluation metrics of studies and machine learning challenges show comparability to human accuracy, but it is debateable if they provide valuable insight for performance and clinical applicability.
  • Real world application: While recent studies show users accept automated classifiers in principleThis glossary term has not yet been described.[15], more in depth early studies showed that only non-experts, and only in ambiguous cases, would change their decision based on an automated system[16]. It is completely unclear whether published metrics accurately reflect this population and cases, and whether automated decisions in those cases can provide beneficial and safe outcomes. An underestimated danger is also that an automated system can of course not diagnose a melanoma that is not being photographed at all, which was in fact the case in a prospective clinica trial in Vienna[17].

DeviceA piece of equipment designed to perform a special function-based automated diagnostics

Apart from image analysis and classification of dermatoscopic images, several other techniques try to solve melanoma diagnosis (list non-exhaustive):

  • Dermatofluoroscopy[18]
  • Multispectral image analysis[19]
  • Raman spectroscopy [20]
  • Electrical Impedance Spectroscopy[21]
  • Genomic analysis through adhesive tape[22]

They all try to provide a binary output with little to no user interaction. Albeit accuracy rates have been reported high for some of those, it is still unclear if and how they will provide meaningful input in clinical practice.

ReferencesThis is material contained in a footnote or bibliography holding further information.
  1. Binder et al.: Application of an artificial neural network in epiluminescence microscopy pattern analysis of pigmented skin lesions: a pilot study. Br. J. Dermatol. 1994;130:460-5. PMID: 8186110.
  2. Menzies et al.: The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. Arch Dermatol 2005;141:1388-96. PMID: 16301386. DOI.
  3. Rajpara et al.: Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma. Br. J. Dermatol. 2009;161:591-604. PMID: 19302072. DOI.
  4. Rubegni et al.: Automated diagnosis of pigmented skin lesions. Int. J. Cancer 2002;101:576-80. PMID: 12237900. DOI.
  5. 5.0 5.1 Esteva et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-118. PMID: 28117445. DOI.
  6. 6.0 6.1 Tschandl et al.: A pretrained neural network shows similar diagnostic accuracy to medical students in categorizing dermatoscopic images after comparable training conditions. Br. J. Dermatol. 2017;177:867-869. PMID: 28569993. DOI.
  7. Marchetti et al.: 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. J. Am. Acad. Dermatol. 2018;78:270-277.e1. PMID: 28969863. DOI.
  8. Haenssle et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 2018;. PMID: 29846502. DOI.
  9. Yu et al.: Acral melanoma detection using a convolutional neural network for dermoscopy images. PLoS ONE 2018;13:e0193321. PMID: 29513718. DOI.
  10. Han et al.: Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J. Invest. Dermatol. 2018;. PMID: 29428356. DOI.
  11. Fujisawa et al.: Deep learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumor diagnosis. Br. J. Dermatol. 2018;. PMID: 29901853. DOI.
  12. Scope et al.: Dermoscopic patterns and subclinical melanocytic nests in normal-appearing skin. Br. J. Dermatol. 2009;160:1318-21. PMID: 19416274. DOI.
  13. Marghoob et al.: Comment on: Screening for malignant melanoma-a critical assessment in historical perspective. Dermatol Pract Concept 2018;8:73-74. PMID: 29785321. DOI.
  14. 14.0 14.1 Navarrete-Dechent et al.: Automated Dermatological Diagnosis: Hype or Reality?. J. Invest. Dermatol. 2018;. PMID: 29864435. DOI.
  15. Fink et al.: Patient acceptance and trust in automated computer-assisted diagnosis of melanoma with dermatofluoroscopy. J Dtsch Dermatol Ges 2018;. PMID: 29927518. DOI.
  16. Dreiseitl et al.: Applying a decision support system in clinical practice: results from melanoma diagnosis. AMIA Annu Symp Proc 2007;191-5. PMID: 18693824.
  17. Dreiseitl et al.: Computer versus human diagnosis of melanoma: evaluation of the feasibility of an automated diagnostic system in a prospective clinical trial. Melanoma Res. 2009;19:180-4. PMID: 19369900. DOI.
  18. Forschner et al.: Diagnostic accuracy of dermatofluoroscopy in cutaneous melanoma detection: results of a prospective multicentre clinical study in 476 pigmented lesions. Br. J. Dermatol. 2018;. PMID: 29569229. DOI.
  19. Hauschild et al.: To excise or not: impact of MelaFind on German dermatologists' decisions to biopsy atypical lesions. J Dtsch Dermatol Ges 2014;12:606-14. PMID: 24944011. DOI.
  20. Lui et al.: Real-time Raman spectroscopy for in vivo skin cancer diagnosis. Cancer Res. 2012;72:2491-500. PMID: 22434431. DOI.
  21. Malvehy et al.: Clinical performance of the Nevisense system in cutaneous melanoma detection: an international, multicentre, prospective and blinded clinical trial on efficacy and safety. Br. J. Dermatol. 2014;171:1099-107. PMID: 24841846. DOI.
  22. Wachsman et al.: Noninvasive genomic detection of melanoma. Br. J. Dermatol. 2011;164:797-806. PMID: 21294715. DOI.