Data Science Bowl 2017 – more AI for medicine and medical images

I was interested to read in the the piece in the MIT Technology Review,

Million-Dollar Prize Hints at How Machine Learning May Someday Spot Cancer

A million dollar prize certain grabbed some headlines, but the details of the winning solution – more image annotations (e.g. more trained doctors / technicians), plus partitioning the basic problem into a) finding nodules; and b) diagnosing cancer), are both clear signposts to the future. Indeed, the future of low-dose CT scans is certainly looking stronger.  And while progress with machine learning, medical imaging, and diagnostic medicine is not always linear (or straightforward, as we read here), 3D imagines that capture relative tissue density and other characteristics clearly provide a highly construct-relevant feature set that is making advances in this are steady and promising (editorial: in a way that other work (e.g. is this argument convincing?) relying on indirect features and characteristics (computational linguistics in this case) is not yet keeping up…).

Since Google’s acquisition of the Kaggle, I have not taken a new look at the Google tool set for creating deep learning networks, but the promise of introducing a “semantic data layer” based on a semantic grammar approach to rubric construction might offer a promising path to better machine understanding of text and speech.

 

 



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