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bioRxiv · 10.1101/381855

Lesion Attributes Segmentation for Melanoma Detection with Deep Learning

Chen, E. Z.·Dong, X.·Wu, J.·Jiang, H.·Li, X.·Rong, R.
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Abstract

Melanoma is the most deadly form of skin cancer worldwide. Many efforts have been made for early detection of melanoma. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to help the diagnosis of melanoma based on dermoscopic images. In this paper, we describe our solutions for the task 2 of ISIC 2018 Challenges. We present two deep learning approaches to automatically detect lesion attributes of melanoma, one is a multi-task U-Net model and the other is a Mask R-CNN based model. Our multi-task U-Net model achieved a Jaccard index of 0.433 on official test data, which ranks the 5th place on the final leaderboard. The code for our solutions is publicly available.

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Chen, E. Z., Dong, X., Wu, J., Jiang, H., Li, X., Rong, R.. 2018-07-31. Lesion Attributes Segmentation for Melanoma Detection with Deep Learning. https://doi.org/10.1101/381855

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