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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