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

 

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”

Kris T. Huang, MD, PhD, CTO

 

Artificial intelligence, deep learning, machine learning – it’s popping up everywhere. When we hear those words, we think massive computers, convolutional neural nets, algorithms, and lifetimes of training data. And yet, my daughter, at 4 years old with zero radiology training, was able to register a pair of 3-dimensional CT scans in seconds, when specialized, purpose-built medical-grade software had difficulty. Machine learning systems are vaguely based on the brain, but clearly there is something missing.

Continue reading “Machine Perception vs. AI”