Lesion segmentation using U-Net network

2018/07/23 by Adrien Motsch, Sébastien Motsch, Motsch, Adrien +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #62P10 #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Cutaneous Melanoma Detection and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1807.08844

openalex publication_date 2018/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper explains the method used in the segmentation challenge (Task 1) in the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards Melanoma Detection challenge held in 2018. We have trained a U-Net network to perform the segmentation. The key elements for the training were first to adjust the loss function to incorporate unbalanced proportion of background and second to perform post-processing operation to adjust the contour of the prediction.

Citations

• Data, Depth, and Design: Learning Reliable Models for Skin Lesion\n Analysis

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