Kris T. Huang, MD, PhD, CTO

Legacy systems, technical debt, and the advantage of starting (mostly) from scratch

In the midst of COVID-19, at Pymedix we’re busy working on 3D medical imaging software for the future of medicine, and we’re gearing up to test our Autofuse pilot product under real-world conditions. Our roots are in radiation oncology, but our perspective is the future of cancer imaging, and medical imaging. We have the strange and wonderful task of thinking inside the box, from the outside of the box. And, being a startup, we started (mostly) from scratch.

 

“Water, water, every where, Nor any drop to drink.”

The Rime of the Ancient Mariner (1834 text)
by Samuel Taylor Coleridge

 

Kris T. Huang, MD, PhD, CTO

Deep learning requires data. Lots of it. There’s lots of medical data, almost 25 exabytes according to IEEE Big Data Initiatives [1], so where’s the problem? The problem is that more than 95% of medical data is unstructured, in the form of raw pixels (90%+) or text, essentially putting it out of reach of large scale analysis.

Continue reading “Data Augmentation”

Kris T. Huang, MD, PhD, CTO

Based on the notion of biological neurons, deep neural networks (DNNs) loosely mimic the networked structure of a (very) simplified brain of sorts. DNNs have revolutionized and automated a number of tasks that were once considered next to intractable, yet we appear to be reaching a plateau as we bump up against the limitations of DNNs. From the opaque nature of neural network models, susceptibility to adversarial attacks, to large data requirements, there are a number of weaknesses uncovered by research that have been quietly reminding us that although pure connectionist models like DNNs mimic biological systems, they remain for the time being rough approximations.

Continue reading “Can An Old Neural Net Learn New Tricks?”

Kris T. Huang, MD, PhD, CTO

Deep learning is a tool. Machine perception is a potential resultant ability, the ability of a machine to interpret data in a manner similar to humans. Being (very) loosely patterned after biological systems, deep neural networks (DNNs) are able to accomplish certain tasks, like image classification or playing Go, with apparent human-like and at times even super-human skill. With performance like that, it is easy to believe (i.e., extrapolate), that its behavior is human-like, or perhaps in some way better.

Continue reading “If Deep Learning Were Human…”