GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

AI makes fake brain scans to teach computers when real ones are scarce

Doctors and researchers often can't get enough labeled medical images, so computers learn slowly or make mistakes.
A type of AI can make realistic looking pictures, these are called synthetic images, and they fill the gaps when real data is missing.
Researchers mixed these fake images with real ones to train programs that spot tissues in brain scans.
The result was small but useful, the programs got better results by a few points, sometimes more when only a handful of scans were available.
It seems simple, add more images, the computer learns more, but making good fake images that still help is tricky.
This approach could speed up research, save time for doctors, and help tools that need lots of data to work well.
More work is needed, yet the idea shows promise; when real scans are rare, AI-made images can be a practical short cut that makes systems smarter and faster.

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GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

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