Deep Learning-Based Unlearning of Dataset Bias for MRI Harmonisation and Confound Removal

Abstract

Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase in variance due to nonbiological factors such as the differences in acquisition protocols and hardware, which can mask signals of interest. We propose a deep learning based training scheme, inspired by domain adaptation techniques, which uses an iterative update approach to aim to create scanner-invariant features while simultaneously maintaining performance on the main task of interest, thus reducing the influence of scanner on network predictions. We demonstrate the framework for regression, classification and segmentation tasks with two different network architectures. We show that not only can the framework harmonise many-site datasets but it can also adapt to many data scenarios, including biased datasets and limited training labels. Finally, we show that the framework can be extended for the removal of other known confounds in addition to scanner. The overall framework is therefore flexible and should be applicable to a wide range of neuroimaging studies. 1. HighlightsO_LIWe demonstrate a flexible deep-learning-based harmonisation framework C_LIO_LIApplied to age prediction and segmentation tasks in a range of datasets C_LIO_LIScanner information is removed, maintaining performance and improving generalisability C_LIO_LIThe framework can be used with any feedforward network architecture C_LIO_LIIt successfully removes additional confounds and works with varied distributions C_LI