New AI technology developments consistently recognize medical image anomalies
A study published in IEEE Access cites the advancements involving the use of AI in medical imaging. Scientists from Skoltech, Philips Research and Goethe University Frankfurt have taken an innovative approach in the pursuit AI implementation by deploying new methods in the setup of AI models.
For this study, researchers used a database of chest x-rays and breast cancer histology microscopy images. Researchers used an autoencoder approach and enacted what the study refers to as “progressive growing training,” which would allow for a small amount of image anomalies to be included in the model setup, with more added on a regular basis. In previous models, no anomalies were present. “We showed some abnormal images to the network to unleash the arsenal of weakly supervised methods, and it helped a lot. Even just one anomalous scan for every 200 normal ones goes a long way.” said Skoltech Professor Dmitry Dylov, senior author of the study, and head of the Institute’s Computational Imaging Group, in a press release sent by Skoltech.
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