Will Dermatologists be Able to Use Deep Learning Based Applications Soon?

Deep Learning is a very hot topic nowadays, either in academia or industry. Big companies like Google and Facebook are relying heavily on Deep Learning, and academia is using this technology to solve complex research problems for its state-of-the-art solutions.

Talking about the usage of Deep Learning with skin cancer (i.e. melanoma), from the academic perspective, we can notice the increased number of publications recently published on the topic. In other words, many algorithms are now employing Deep Learning to solve skin cancer issues, like melanoma detection for instance.

Deep Learning in itself is not new. The earliest Deep Learning like algorithms can be traced back to 1965. What is very new however is the use of Deep Learning techniques for melanoma detection. If you search for research papers employing Deep Learning for melanoma detection, you will notice that they were published between 2015 and 2016 only! With most of the papers published in 2016. A thesis in Sweden has even been written on this topic this year also, titled: To be, or not to be melanoma. Details on algorithms developed during those 2-years will not be explained here as they are beyond the aim of this post. But, you got the point.

Although the short period on targeting melanoma detection using Deep Learning, open source code has been shared with the community like SkinDeep and DL8803.

But the use of Deep Learning for melanoma detection has even went beyond that in such very short period of time, as you will see in a moment. John R. Smith, Manager of Multimedia and Vision at IBM T. J. Watson Research Center, mentions that one of the most promising near-term applications of automated image processing is in detecting melanoma. Steve smith in his article adds that IBM Watson will be using Deep Learning to study and diagnose melanoma, allowing Watson to detect important (sometimes missed) features of the disease.

Bridging the gap between research and industry is sometimes a long-term and daunting task. However, the use of Deep Learning with melanoma is showing the opposite. For instance, EXB, a German company in Munich, has utilized its image processing technology which is based on the latest developments in Deep Learning techniques, to win the ISIC Challenge for automated skin lesion analysis and melanoma detection this year.

In a previous article, I have shown how Deep Learning and mobile technology together are able to transform melanoma detection. This can be seen in SkinIQ, a company found last year which utilizes Deep Learning algorithms coupled with mobile smartphone technology for the diagnosis of skin lesions.

With this pace of utilizing Deep Learning for melanoma detection, and with FDA and other regulations procedures being accelerated, it seems that dermatologists will be utilizing such applications very soon.