Deep Learning to Transform Melanoma Detection: It is Closer Than You Think

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In an interview I conducted with Jürgen Schmidhuber, a German computer scientist and artist known for his work in many fields including machine learning, Artificial Intelligence (AI), and artificial neural networks, I asked him where he sees Deep Learning (a form of machine learning) in the coming 10-years. I got an interesting answer to this question which I would like to share a part of in this post. Jürgen mentioned that by 2025, even minor extensions of existing machine learning and neural network algorithms will achieve many important superhuman feats.

He also added that he expects huge recurrent neural networks (RNNs) on dedicated hardware to simultaneously perceive and analyse an immense number of multimodal data streams (speech, texts, video, many other modalities) from many sources, learning to correlate all those inputs and use the extracted information to achieve a myriad of commercial and non-commercial goals. Those RNNs will continually and quickly learn new skills on top of those they already know. This should have innumerable applications, and will change society in innumerable ways.

Jürgen even took it further when he told me that the one thing which seems to be clear is that in the not too distant future, supersmart AIs will start to colonize the solar system, and within a few million years the entire galaxy! The universe wants to make its next step towards more and more unfathomable complexity.

Before proceeding further, let me quickly tell you what we mean by Deep Learning for those new to this term. Deep Learning is considered a leading machine learning tool, and is an improvement of artificial neural networks such that it consists of more layers allowing higher levels of abstraction and improved predictions from data.

Talking about medical images (i.e. skin), the ultimate goal of applying machine learning to such images is to recognize patterns in a better and quicker way than humans can, and thus increasing the productivity of doctors and patient healthcare outcomes. Deep Learning takes that further, especially its ability to provide improved predictions from the large amount of data it is trained on due to the higher levels of abstractions it provides. Deep Learning is believed to effectively contribute to the early detection of melanoma and facilitate in distinguishing between benign and malignant moles, as it is considered the leading machine learning tool in the imaging and computer vision domains, and has shown its capability to provide state-of-the-art results in different challenging tasks.

Trends are now moving towards diagnosis without doctors, and Deep Learning is playing a big role in such transformation. As scary or fanciful it might sound, such effects seem to be coming sooner than we think. This already applies for radiology for instance, as Deep Learning algorithms have the ability to start producing radiology reports for basic studies (i.e. mammography) in around 5-years.

Coming back to melanoma, I believe that such diagnosis transformation will be coming very soon, as not only Deep Learning will play the role in such transformation, but also mobile technology. Yes, mobile technology is also changing healthcare, and mobile phones can now be viewed as medical devices themselves.

Let me give an example on how Deep Learning and mobile technology could come together to facilitate the transformation. In an Apple ResearchKit press release announcement end of last year, it showed how Oregon Health and Science University is using the ResearchKit to study whether digital images taken in an iPhone can be used to learn more about melanoma risks and manage skin health. Add to that the availability of Google’s Artificial Intelligence platform -TensorFlow- on mobile devices making the mobile device an ideal platform for tackling the melanoma diagnosis and detection issues.

Efforts have actually already started in bringing melanoma to the mobile phone through different startups, let alone that doctors now are able to prescribe apps! So, I believe that not only the melanoma diagnosis transformation is so close, but it is actually knocking our doors, do you agree with that?

Leveraging machine/deep learning and image processing in medical image analysis. You can kindly find more information about me on my website:

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