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Biotechnology

This algorithm generates tumors to help fight cancer

September 17, 2018

Researchers have built a system to create a larger and more diverse data set on which to train medical AI.

You’re only as effective as your data set: Most artificial-intelligence programs rely on a large set of information to learn from. But if the data isn’t representative of all populations or circumstances, the system could be biased or ineffective.

The news: A new study out from chip company Nvidia, the Mayo Clinic, and the MGH & BWH Center for Clinical Data Science has created an algorithm that produces a more diverse set of medical data. Using generative adversarial networks (or GANs), synthetic scans depicting abnormalities can be created from existing MRIs of brain tumors.

Why it matters: “Diversity is critical to success when training neural networks, but medical imaging data is usually imbalanced,” Hoo Chang Shin, a research scientist at Nvidia, told ZDNet. “There are so many more normal cases than abnormal cases, when abnormal cases are what we care about, to try to detect and diagnose.”

Deep Dive

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Brain interfaces could let paralyzed people speak at almost normal speeds.

Forget designer babies. Here’s how CRISPR is really changing lives

The gene-editing tool is being tested in people, and the first treatment could be approved this year.

Neuroscientists listened in on people’s brains for a week. They found order and chaos.

The study shows that our brains exist between chaos and stability—a finding that could be used to help tweak them either way.

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Illustration by Rose Wong

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