RT Journal Article
SR Electronic
T1 Lesion Attributes Segmentation for Melanoma Detection with Deep Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 381855
DO 10.1101/381855
A1 Chen, Eric Z.
A1 Dong, Xu
A1 Wu, Junyan
A1 Jiang, Hongda
A1 Li, Xiaoxiao
A1 Rong, Ruichen
YR 2018
UL http://biorxiv.org/content/early/2018/09/10/381855.abstract
AB Melanoma is the most deadly form of skin cancer world-wide. Many efforts have been made for early detection of melanoma. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to improve the diagnosis of melanoma based on dermoscopic images. In this paper, we describe our solution for the task 2 of ISIC 2018 Challenges. We present a multi-task U-Net model to automatically detect lesion attributes of melanoma. Our multi-task U-Net deep learning model achieves a Jaccard index of 0.433 on official test data, which ranks the 5th place on the final leaderboard.