The high performance reported by Deep Convolutional Neural Networks (CNNs) on image classification, detection and segmentation are contributing to its usage increase, including the CNNs applied to Medical Image. To automatically detect motion artifacts on MRI we fine-tuned four different CNNs. Visualizing the features extracted by the CNN is crucial to understand the reported result from each architecture. Using a gradient-based visualization method, we noticed that all architectures have salient points on Cerebrospinal Fluid (CSF) and background. Furthermore, the architectures that reported better results also extract information from white-matter, confirming that this anatomical structure has essential information regarding the task.
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