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In this work we examine the performance enhancement in classification ofmedical imaging data when image features are combined with associated non-imagedata. We compare the performance of eight state-of-the-art deep neural networksin classification tasks when using only image features, compared to when theseare combined with patient metadata. We utilise transfer learning with networkspretrained on ImageNet used directly as feature extractors and fine tuned onthe target domain. Our experiments show that performance can be significantlyenhanced with the inclusion of metadata and use interpretability methods toidentify which features lead to these enhancements. Furthermore, our resultsindicate that the performance enhancement for natural medical imaging (e.g.optical images) benefit most from direct use of pre-trained models, whereas nonnatural images (e.g. representations of non imaging data) benefit most fromfine tuning pre-trained networks. These enhancements come at a negligibleadditional cost in computation time, and therefore is a practical method forother applications.

Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion