PT  - JOURNAL ARTICLE
AU  - Jiang, Hongda
AU  - Rong, Ruichen
AU  - Wu, Junyan
AU  - Li, Xiaoxiao
AU  - Dong, Xu
AU  - Chen, Eric Z
TI  - Skin Lesion Segmentation with Improved C-UNet Networks
AID  - 10.1101/382549
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 382549
4099  - http://biorxiv.org/content/early/2018/08/01/382549.short
4100  - http://biorxiv.org/content/early/2018/08/01/382549.full
AB  - —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.