*|MC:SUBJECT|* *|MC_PREVIEW_TEXT|*
Hello!
With the big hype Deep Learning has been gaining in recent years, I
found myself kind of *forced* to start employing it in my research.
Well, I don't have any problem with that of course, on the contrary, it
is indeed very interesting and have shown to solve many issues that
traditional methods couldn't.
The dilemma I faced was that should I use Deep Learning in /image/
/segmentation/ or not? Especially that I have been utilizing image
processing based techniques for such task with no issues so far. But
again, everyone is talking about Deep Learning and it's kind of hard to
imagine a publication without it at this point.
So, here I am, using Deep Learning in segmenting skin lesion images in
my papers below:
*A Deep Learning Based Approach to Skin Lesion Border Extraction With a
Novel Edge Detector in Dermoscopy Images*
<https://dspace.stir.ac.uk/retrieve/21516e25-9ceb-41e5-a514-295a26c4120c/IJCNN%20paper.pdf>
*Supervised Versus Unsupervised Deep Learning Based Methods for Skin
Lesion Segmentation in Dermoscopy Images*
<https://dspace.stir.ac.uk/bitstream/1893/29692/1/CanadianAI19_submitted.pdf>
I cannot say that the results are bad. But, to be honest, I returned
back to my old habit in subsequent papers (which I will share with you
soon) in using image processing based techniques for skin lesion
segmentation. And, guess what? They actually outperformed U-Net at least
and I have made such comparison clear in the papers to highlight that.
Well, that's tt for now...
You can kindly find our other work related to skin lesions and melanoma
at our project website: *DeepDerma* <https://www.deepderma.ai/>.
Oh, didn't I say that we are always open for collaboration. If you would
like to work with us or have any thoughts or projects you would like me
to get involved with you in, please don't hesitate to shoot me an email
and we can take it from there.
Best,
Abder-Rahman
*abder.io* <https://abder.io/>
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