March 4, 2021
Previous Next

'Self-trained' deep learning to improve disease diagnosis

Jeremy Thomas/LLNL

New work by computer scientists at LLNL and IBM Research on deep learning models to accurately diagnose diseases from X-ray images with less labeled data won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference on February 19. The technique, which includes novel regularization and “self-training” strategies, addresses some well-known challenges in the adoption of artificial intelligence for disease diagnosis, namely the difficulty in obtaining abundant labeled data due to cost, effort or privacy issues and the inherent sampling biases in the collected data, researchers said. AI algorithms also are not currently able to effectively diagnose conditions that are not sufficiently represented in the training data. Read more at LLNL News.