@inproceedings{6a05699a5da54966b056031ceb48b55e,
title = "Lesion attributes segmentation for melanoma detection with multi-task u-net",
abstract = "Melanoma is the most deadly form of skin cancer worldwide. Many efforts have been made for early detection of melanoma with deep learning based on dermoscopic images. It is crucial to identify the specific lesion patterns for accurate diagnosis of melanoma. However, the common lesion patterns are not consistently present and cause sparse label problems in the data. In this paper, we propose a multi-task U-Net model to automatically detect lesion attributes of melanoma. The network includes two tasks, one is the classification task to classify if the lesion attributes present, and the other is the segmentation task to segment the attributes in the images. Our multi-task U-Net model achieves a Jaccard index of 0.433 on official test data of ISIC 2018 Challenges task 2, which ranks the 5th place on the final leaderboard.",
keywords = "Deep learning, Melanoma, Multi-task learning, Skin cancer, U-Net",
author = "Chen, {Eric Z.} and Xu Dong and Xiaoxiao Li and Hongda Jiang and Ruichen Rong and Junyan Wu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/ISBI.2019.8759483",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "485--488",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
}