Introduction to computer assisted diagnosis[edit | edit source]
The early diagnosis of melanoma remains challenging. Meta-analysis has demonstrated that the use of dermoscopy by trained users improves diagnostic accuracy for melanoma compared to naked-eye examination alone, mainly by increasing sensitivity.   As non-physicians detect the majority of melanomas and population-based melanoma screening by clinicians is not currently recommended  outside of Germany, there is interest in the development of dermoscopy image analysis algorithms to aid laypersons or non-dermatology physicians in melanoma detection.    
A recent landmark paper demonstrates the potential of ‘deep learning’ algorithms to aid in melanoma diagnosis. In their study, the researchers used deep convolutional neural networks (CNNs) which they trained in a large dataset of clinical images and then compared against 21 dermatologists for the binary classification of keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi, with the CNN achieving performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. While the potential for computer assisted diagnosis is great, the lack of a large public dataset of validated skin images has limited the ability to directly compare the diagnostic performance of automated image analysis algorithms across studies and against clinicians. The ISIC Archive (link available on dermoscopedia menu bar) was created to address this need and has hosted consecutive international ‘Challenges’ that have engaged the broader computer science community.
There are many potential barriers to the implementation of automated diagnosis in clinical care (e.g., vetting of the accuracy of the algorithms, potential for overdiagnosis and false reassurance, acceptance by the medical community) but it is logical to expect that some form of computer assisted triage or diagnosis is forthcoming in the near future. There are already hundreds of ‘apps’ for mobile devices that are being marketed as educational or assistive devices for melanoma early detection. The medicolegal and social landscape around the adoption of these apps is rapidly changing.
Artificial intelligence (AI) algorithms have been developed in order to improve diagnostic accuracy of melanoma. They have been recently shown to be reliable tools, classifying melanoma with a level of competence comparable to a dermatologist .
In a recent study on 1550 images of skin lesions, more than half of the melanoma diagnoses were either in situ or <1 mm deep, indicating that the studied algorithm could play a role in detecting thin or early-stage lesions. Of course, diagnostic specificity varied with the camera used to photograph the lesions .
Automated diagnosis[edit | edit source]
Neural networks[edit | edit source]
Machine learning techniques, and neural networks in particular, for diagnosing melanoma have been in focus of research for more than two decades in dermatology. A systematic review as early as 2009 has shown that automated classifiers can have a comparable diagnostic odds ratio to physicians. Systems reviewed in this case commonly relied on manual or semi-automatic feature extraction of digital images. Unprecedented image classification 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[edit | edit source]
Several studies found human level performance of such state-of-the-art CNNs to dermatologists for melanoma diagnosis or multi-class accuracy. Similar rates were found for classification of close-up images.
A more current endeavour was comparing multi-class accuracy of the ISIC 2018 challenge winners to human readers in a world wide and open online game. This study  showed for the first time that even average machine learning algorithms can show a higher accuracy in a multi-class setting encompassing all relevant pigmented lesion types.
Datasets[edit | edit source]
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:
- HAM10000, available for download at the Harvard Dataverse and ISIC-Archive
- EDRA Interactive Atlas of Dermoscopy
- PH2 Database
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 and others are available in a structured way.
Problems[edit | edit source]
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. 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.
- 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 non-commercial example application can be used at ypsono.com.
- 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. Also, a recent Meta-Analysis has shown significant bias of metrics connected to known test datasets
- Real world application: While recent studies show users accept automated classifiers in principle, more in depth early studies showed that only non-experts, and only in ambiguous cases, would change their decision based on an automated system. 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.
Device-based automated diagnostics[edit | edit source]
Apart from image analysis and classification of dermatoscopic images, several other techniques try to solve melanoma diagnosis (list non-exhaustive):
- Multispectral image analysis
- Raman spectroscopy 
- Electrical Impedance Spectroscopy
- Genomic analysis through adhesive tape
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.
References[edit | edit source]
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