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Abstract #2950

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

Deepa Anand1, Dattesh Shanbhag1, Chitresh Bhushan2, and Uday Patil3
1GE Healthcare, Bangalore, India, 2GE Healthcare, Niskayuna, NY, United States, 3GE Healthcaer, Bangalore, India

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceMulti-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%).

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