Tag: machine perception
“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
Machine learning, and in particular so-called “deep learning,” is an undeniably powerful tool that has revolutionized certain types of classification problems, notably image/object recognition, speech recognition and synthesis, and automated language translation. The lay terms used in association with the field, like “neural,” “intelligence,” “deep,” and “learning,” evoke mental images of something brain-like or mind-like floating around in our computers, akin to an ancestor of HAL 9000. Combine these impressions with classification accuracies that rival or sometimes exceed human performance, and it is understandably convenient to ascribe human-like perceptual abilities to it.
Kris T. Huang, MD, PhD, CTO
Have you ever wondered how far we’ve come in image registration technology? It wasn’t always the case that 3D deformable registration could be fully automated. This week, we’re throwing it back to 1895, when medical imaging started, then we’ll go back to the future to see how medical image registration evolved.
Continue reading “A Brief History of Image Registration: Part 1”
Today we’re lifting the curtain on the machine perception technology behind Autofuse, and showing how it works step by step in a complicated head and neck case and a pediatric spine growth case using scans taken 31 months apart! We also provide a glimpse of the future of patient-specific QA for deformable registration, and how Autofuse technology can be used to monitor how radiation treatment to the spine affects vertebral body growth in children.
Continue reading “Video Look Behind the Curtain at Autofuse”
Kris T. Huang, MD, PhD, CTO
In our last post, I introduced (perhaps more like re-introduced) the concept of machine perception (MP) as sort of a forgotten second cousin of machine learning (ML). Ironically, one of the first forms of neural network was dubbed the “perceptron,” a nod to the link between perception and intelligence. And while MP lives on and thrives today in fields like computer vision, it isn’t necessarily tied to ML.
Continue reading “Perception as the Prerequisite for Intelligence”