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Practical Guidelines for Optimizing and Accurate Sizing of Medical Imaging Workflows

Anas Abidin , NVIDIA | Christopher Bridge, Center for Clinical Data Science

GTC 2020

Deep dive into the various considerations that could be critical to substantially improve existing medical imaging workflows. We'll motivate our discussions through a detailed analysis of two common medical imaging workflows: 2D classification and 3D segmentation. Our results suggest that controlling for aspects such as i/o format, selection of hyper-parameters, and mixed-precision training could all be key to maximizing hardware performance and reducing turnaround time for experiments. Furthermore, we aim to discuss other strategies, such as learning-rate warmup and scaling, effect of optimizers, scaling to multiple GPUs, and their potential effects on training throughput. We were able to show a 5x improvement for the 2D classification task through our ablation experiments. We'll use these results suggest best practices learned, and also build a sizing calculator for providing quantitative insights for hardware investments.

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