Monash University researchers have recently developed a co-training AI algorithm for medical imaging, aimed at emulating the process of seeking a second opinion. This advancement, published in Nature Machine Intelligence, addresses the scarcity of human annotated medical images by utilizing an innovative adversarial learning approach with unlabelled data.
The research involved creating a "dual-view" AI system where two components compete in a unique manner.
The first part of the AI system emulates radiologists' expertise by labelling medical images, while the second part evaluates the quality of AI-generated labelled scans by comparing them against the limited annotated scans provided by human radiologists. This intelligent interplay between the two components enhances the AI's ability to provide accurate assessments, resembling the valuable process of obtaining a second opinion from a human expert.
The researchers at Monash University have developed an innovative algorithm that enables multiple AI models to capitalize on the strengths of both labeled and unlabeled data. By learning from each other's predictions, the AI models enhance overall accuracy.
In extensive testing across three publicly accessible medical datasets, using just 10 percent labeled data, the algorithm demonstrated an average improvement of 3 percent compared to the latest state-of-the-art approach under identical conditions, as revealed by Himashi Peiris, a Ph.D. candidate at Monash University’s Faculty of Engineering.