“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

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…”

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.

Continue reading “We Can Do Better”

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.

Continue reading “Medicine, Deep Learning, and Black Boxes”