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|Automated diagnosis||This page gives a brief introduction and overview on neural network based automated diagnosis of skin lesions with dermatoscopy.|
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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
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 classificatin of close-up images.
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.
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 example application provided by the ViDIR Group (Medical University of Vienna) 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.
- 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
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.
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