UCLA Researchers have developed a method to change the apparent race of faces in datasets that are used to train medical machine learning systems, in

Synthetic Data: Changing Race In Facial Images To Address Bias In Medical Datasets

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2021-06-15 10:30:12

UCLA Researchers have developed a method to change the apparent race of faces in datasets that are used to train medical machine learning systems, in an attempt to redress the racial bias that many common datasets suffer from.

The new technique is capable of producing photorealistic and physiologically accurate synthetic video at an average rate of 0.005 seconds per frame, and is hoped to aid the development of new diagnostics systems for remote healthcare diagnosis and monitoring – a field that has expanded greatly under COVID restrictions. The system is intended to improve the applicability of remote photoplethysmography (rPPG), a computer vision technique that evaluates facial video content to detect volumetric changes in blood supply in a non-invasive manner.

Though the work, which utilizes convolutional neural networks (CNNs), incorporates previous research code published by the UK’s Durham University in 2020, the new application is intended to preserve pulsatile signals in the original test data, rather than just visually changing the apparent race of the data, as the 2020 research does.

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