Source
"Sex Difference in the brain: Divergent results from traiditional machine learning and convolutional networks"
Purpose
This research was conducted for the IEEE International Symposium on Biomedical Imaging (ISBI). ISBI is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. It fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS).
Abstract
Neuroimaging research has begun adopting deep learning to model structural differences in the brain. This is a break from previous approaches that rely on derived features from brain MRI, such as regional thicknesses or volumes. To date, most studies employ either deep learning based models or traditional machine learning volume based models. Because of this split, it is unclear which approach is yielding better predictive performance or if the two approaches will lead to different neuroanatomical conclusions, potentially even when applied to the same datasets. In the present study, we carry out the largest single study of sex differences in the brain using 21,390 UK Biobank T1-weighted brain MRIs analyzed through both traditional and 3D convolutional neural network (3D-CNN) models. Through comparing performances, we find that 3D-CNNs, which derive 20 deep learning brain features, out perform traditional machine learning models using 20 brain region volumes as features. Through comparing regions highlighted by both approaches, we find good internal consistency between traditional machine learning and deep learning models, but poor overlap between these approaches. In summary, we find that 3D-CNNs show exceptional predictive performance, but may highlight neuroanatomical regions different from what would be found by volume-based approaches