论文标题
用于脱毛的编码器Decoder CNN在皮肤镜图像中脱毛
An Encoder-Decoder CNN for Hair Removal in Dermoscopic Images
论文作者
论文摘要
去除堵塞头发的过程在皮肤癌的早期和准确诊断中具有相关作用。它包括检测头发并恢复其下面的质地，偶尔会遮挡。在这项工作中，我们提出了一个基于卷积神经网络的模型，用于在皮肤镜图像中脱毛。在网络培训期间，我们使用合并的损失函数来提高所提出模型的恢复能力。为了训练CNN并定量验证其性能，我们模拟了从ph2，Dermquest，Dermquest，Dermis，Edra2002和ISIC数据存档中提取的无毛图像中的皮肤的存在。据我们所知，没有其他基于深度学习的脱毛方法。因此，我们通过基于传统的计算机视觉技术的六种最先进算法进行比较，通过比较参考无毛图像的相似度量，并与头发模拟相似。最后，使用统计检验来比较方法。定性和定量结果都证明了我们网络的有效性。
The process of removing occluding hair has a relevant role in the early and accurate diagnosis of skin cancer. It consists of detecting hairs and restore the texture below them, which is sporadically occluded. In this work, we present a model based on convolutional neural networks for hair removal in dermoscopic images. During the network's training, we use a combined loss function to improve the restoration ability of the proposed model. In order to train the CNN and to quantitatively validate their performance, we simulate the presence of skin hair in hairless images extracted from publicly known datasets such as the PH2, dermquest, dermis, EDRA2002, and the ISIC Data Archive. As far as we know, there is no other hair removal method based on deep learning. Thus, we compare our results with six state-of-the-art algorithms based on traditional computer vision techniques by means of similarity measures that compare the reference hairless image and the one with hair simulated. Finally, a statistical test is used to compare the methods. Both qualitative and quantitative results demonstrate the effectiveness of our network.
