Realizing a robust multi-task training strategy with deep learning: application in Spine MR image quality assessment

Multi-task training is attractive in AI applications (from memory, processing time and potential data reduction), where tasks have commonality in terms of features, but still requires differentiation for individual outputs derived. In this work we present a methodology to implement robust multi-task learning framework considering various strategies (parallel, iterative and sequential). We tested the approach for image quality assessment of spine MRI localizer images. We demonstrate that the sequential training is the most effective, in preserving an accuracy above the acceptable level while allowing for a save in number of model parameters (50%).