Author: pymedix-webadmin
“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.
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.
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.
Sarah Kim, MD, CEO
Losing a loved one to cancer can be one of the hardest moments in your life. It might be difficult to be optimistic. I lost my aunt to breast cancer some 10 years ago. She was diagnosed in one country, but passed away in another. Even today, her memories keep me wondering whether there was anything that we could have done more to help prevent cancer in the first place, tried to catch it earlier, provided better treatment, or at least made her journey throughout more comfortable.
Kris T. Huang, MD, PhD, CTO
Make no mistake, neural networks are powerful tools. This class of algorithms single-handedly brought about drastic and rapid advancement in tasks like classification, speech recognition, and natural language processing, bringing about the end of the second “AI winter” that lasted from the late 1980s until around the late 2000s.