This paper summarizes the method used in our submission to Task 1 of the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis Towards Melanoma Detection challenge held in 2018. We used a fully automated method to accurately segment lesion boundaries from dermoscopic images. A U-net deep learning network is trained on publicly available data from ISIC. We introduce the use of intensity, color, and texture enhancement operations as pre-processing steps and morphological operations and contour identification as post-processing steps.
翻译:本文概述了我们提交2018年国际皮肤成像协作机制(ISIC)对梅兰诺马探测挑战的皮肤色素分析任务1时所使用的方法,我们使用完全自动化的方法准确分解脱温图像的损害界限,对U-net深层学习网络进行了关于国际标准工业分类公开数据的培训,我们采用强度、颜色和质地增强操作作为预处理步骤和形态操作,并将等式识别作为后处理步骤。