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Abstract #3535

Informed deep convolutional neural networks

R. Marc Lebel1 and Daniel Litwiller2
1Applications and Workflow, GE Healthcare, Calgary, AB, Canada, 2Applications and Workflow, GE Healthcare, New York, NY, United States

Convolutional neural networks are an emerging tool in medical imaging. Conventional CNNs accept an image as input and return a task-specific output (e.g., a filtered image, a disease probability). Conventional CNNs struggle to generalize or perform poorly when image data alone is insufficient to solve the problem. We propose three ways to incorporate relevant scan information into a CNN. The value of this method is demonstrated on rSOS image denoising, a previously unstable problem.

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