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Training Models 20x Faster on GPU in Medical Image Analysis

Dong Yang, NVIDIA

GTC 2020

Analyzing high-dimensional medical images (2D/3D/4D CT, MRI, histopathological images, and so on) plays an important role in many biomedical applications, such as anatomical pattern understanding, disease diagnosis, and treatment planning. The AI-assisted models have been widely adopted in the domain of medical image analysis with great successes. However, training such models with large-size data is expensive in terms of computation and memory consumption. We'll provide solutions for improving model-training efficiency, which will speed up the training of AI models by 20x and enable researchers and radiologists to improve the efficiency in their clinical studies. The overall efficiency improvement comes from both improved algorithms and engineering advance. Needless to say, this work is also an excellent showcase for the collaboration among different NVIDIA teams.

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