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Deep learning has made major strides in medical imaging segmentation in the last several years for its automated feature extraction. This model fitting process is susceptible to over-fitting, and can benefit from sparsity. Here, we show theoretical and experimental potential of using low-entropy images as sparse input to improve deep learning driven tissue segmentation, using tumor and heart segmentation problems as exemplary cases.
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