Tag: deep learning

We Can Do Better

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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.

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Medicine, Deep Learning, and Black Boxes

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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.

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Machine Learning Adversarial Attacks: It’s All Fun and Games Until Someone Gets Hurt

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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.

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A Brief History of Image Registration: Part 3

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Kris T. Huang, MD, PhD, CTO

The 1990s brought progress from chamfer matching to voxel intensity, and it ushered in the idea that intensity relationships between images could be non-linear, hinting at the concept of a more general dependence between images.

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A Brief History of Image Registration: Part 2

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Kris T. Huang, MD, PhD, CTO

Last time, we took a brief stroll through the timeline of radiology to take a look at the relationship between the evolution of imaging technologies and corresponding development in medical image registration. Digital radiology took root with the clinical introduction of CT in 1971, and despite the introduction of clinical MRI in 1980, it wasn’t until PET came of age in the early 1990s that the utility of image registration became plainly evident. Today we’ll dig into a couple of the early algorithms.

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