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in which I and P suggest the input impression and its corresponding prediction map, respectively, f⋅ denotes the U-Net with the network parameter φ.
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Table six showed the effects in the produced method in extracting the remaining and proper lungs from Xray pictures applying boundary uncertainty maps in 3 different ways. As demonstrated by the effects, our designed technique received the bottom segmentation functionality, with the typical DS of 0.9437 when basically skilled on boundary uncertainty maps, but it had amplified functionality when combining the uncertainty maps with the first images or their qualifications excluded version for network instruction (with the common DS of 0.
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. The prediction map was somewhat coarse as in contrast with manual annotations get more info of objects since the U-Web has a straightforward network construction and therefore minimal potential to deal with illustrations or photos with varying characteristics.
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