Skin Lesion Segmentation with Improved C-UNet Networks
Hongda JiangiD, Ruichen RongiD, Junyan WuiD, Xiaoxiao Li, Xu Dong, Eric Z Chen
Abstract
--This paper proposes an innovative method for Part1, skin lesion segmentation of the ISIC 2018 Challenge. Our network C-UNet is based on UNet network, we combined several methods on this basic network which made some improvements on Jaccard Index ultimately, our method yield an average Jaccard Index of 0.77 on the On-line validation dataset.
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