A New Noisy Label Learning Method for Accurate Brain Age Prediction

Brain age prediction using neuroimaging data is crucial for understanding the mechanisms of brain aging. However, many previous methods for predicting brain age have relied on chronological age as the guiding label for model training. The models produced may not accurately predict true brain age. To overcome this problem, we propose a new noisy label learning method to train a robust brain age prediction model by addressing the possible label bias in age. The method incorporates two deep neural networks and their corresponding mean-teacher networks, which effectively identify and leverage samples with clean and noisy labels that show consistency and inconsistency between brain age and chronological age, respectively. Experiment using simulated datasets including 10,000 samples demonstrates high predictive accuracy, with a correlation coefficient greater than 0.9 between the predicted brain age and the simulated brain age. Compared with traditional regression algorithms, our method obtains better performance for the simulated datasets. We applied our method to brain functional connectivity data of 10,000 healthy controls from the UK Biobank dataset to validate the capability of our method. Results show that the predicted brain ages have greater correlations with cognitive measures compared to the chronological age. In summary, our new noisy label learning method can effectively predict brain age using neuroimaging data by overcoming potential age bias.