论文标题
基于照明的转化改善了皮肤镜图像中的皮肤病变细分
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
论文作者
论文摘要
皮肤病变的语义分割是计算机辅助诊断皮肤图像的重要初始任务。尽管基于深度学习的方法已大大提高了细分精度，但仍可以通过解决主要挑战（例如病变形状，大小，颜色和变化级别的对比度的变化）来改进的余地。在这项工作中，我们提出了第一个深层语义分割框架，用于将皮肤镜图像以及原始的RGB图像以及使用皮肤照明和成像物理学提取的信息。特别是，我们结合了来自特定颜色带，照明不变灰度图像和阴影衰减图像的信息。我们在三个数据集上评估了我们的方法：ISBI ISIC 2017皮肤病变细分挑战数据集，Dermofit Image库和PH2数据集，并观察到仅在接受RGB图像的基线模型的平均JACCARD指数中，在平均JACCARD指数中的改善分别为12.02％，4.30％和8.86％。
The semantic segmentation of skin lesions is an important and common initial task in the computer aided diagnosis of dermoscopic images. Although deep learning-based approaches have considerably improved the segmentation accuracy, there is still room for improvement by addressing the major challenges, such as variations in lesion shape, size, color and varying levels of contrast. In this work, we propose the first deep semantic segmentation framework for dermoscopic images which incorporates, along with the original RGB images, information extracted using the physics of skin illumination and imaging. In particular, we incorporate information from specific color bands, illumination invariant grayscale images, and shading-attenuated images. We evaluate our method on three datasets: the ISBI ISIC 2017 Skin Lesion Segmentation Challenge dataset, the DermoFit Image Library, and the PH2 dataset and observe improvements of 12.02%, 4.30%, and 8.86% respectively in the mean Jaccard index over a baseline model trained only with RGB images.
