论文标题
用于皮肤病变定位和分割的检测器分段网络
Detector-SegMentor Network for Skin Lesion Localization and Segmentation
论文作者
论文摘要
黑色素瘤是一种威胁生命的皮肤癌，当时未诊断早期未诊断。尽管非黑色素瘤癌症的病例比黑色素瘤癌更具致命性。早期发现黑色素瘤对于及时诊断黑色素瘤癌症至关重要，并禁止其扩散到遥远的身体部位。皮肤病变的分割是皮肤镜图像中癌变分类的黑色素瘤癌症的关键步骤。皮肤皮肤图像的手动分割非常耗时且容易出错，导致迫切需要智能，准确的算法。在这项研究中，我们提出了一种基于简单但新颖的网络中网络卷积神经网络（CNN）的方法来分割皮肤病变。基于更快的区域CNN（更快的RCNN）用于预处理，以预测整个图像中病变的边界框，随后将其裁剪并馈入分割网络以获得病变掩模。分割网络是UNET和沙漏网络的组合。我们在ISIC 2018数据集上培训和评估了我们的模型，并在ph \ textsuperscript {2}和ISBI 2017数据集上进行了交叉验证。我们提出的方法超过了最先进的方法，其骰子相似系数为0.915，ISIC 2018数据集和骰子相似性系数为0.947，精度为0.959，而ISBI 2017数据集上的精度为0.971。
Melanoma is a life-threatening form of skin cancer when left undiagnosed at the early stages. Although there are more cases of non-melanoma cancer than melanoma cancer, melanoma cancer is more deadly. Early detection of melanoma is crucial for the timely diagnosis of melanoma cancer and prohibit its spread to distant body parts. Segmentation of skin lesion is a crucial step in the classification of melanoma cancer from the cancerous lesions in dermoscopic images. Manual segmentation of dermoscopic skin images is very time consuming and error-prone resulting in an urgent need for an intelligent and accurate algorithm. In this study, we propose a simple yet novel network-in-network convolution neural network(CNN) based approach for segmentation of the skin lesion. A Faster Region-based CNN (Faster RCNN) is used for preprocessing to predict bounding boxes of the lesions in the whole image which are subsequently cropped and fed into the segmentation network to obtain the lesion mask. The segmentation network is a combination of the UNet and Hourglass networks. We trained and evaluated our models on ISIC 2018 dataset and also cross-validated on PH\textsuperscript{2} and ISBI 2017 datasets. Our proposed method surpassed the state-of-the-art with Dice Similarity Coefficient of 0.915 and Accuracy 0.959 on ISIC 2018 dataset and Dice Similarity Coefficient of 0.947 and Accuracy 0.971 on ISBI 2017 dataset.
