Astronomers Teach a Machine To Analyze Space Images

New submitter Jim Geach writes: Our team of astronomers and computer scientists has developed a novel unsupervised machine learning algorithm — a combination of Growing Neural Gas and Hierarchical Clustering — to automatically analyze astronomical images. In effect, the algorithm performs the same task as a human 'eyeballing' an image, automatically identifying and labeling the points of interest. We're aiming to deploy the algorithm on the next generation of astronomical surveys such as LSST and Euclid where no human, or even group of humans, could closely inspect every piece of data. The algorithm could also find application in other fields, such as medical imaging and early disease diagnosis. The results are being presented at the UK National Astronomy Meeting in Wales, and the details of the algorithm are described in this paper.

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Automated computer assisted scanning

[sjbe]

The algorithm could also find application in other fields, such as medical imaging and early disease diagnosis.

Radiologists already use software that assists in scanning images for potentially interesting features. They aren't a replacement but they apparently do a fairly good job at helping to ensure as little as possible gets overlooked. I did some consulting work in a radiology clinic some years ago and they used this technology there to good effect.

I wouldn't be surprised to see anatomic pathologists using technology like this somewhere in the future. The logistics of it are much more complicated than for radiology but I think somewhere down the line it will probably happen.