论文标题
病变网 - 使用坐标卷积和深残余单位的皮肤病变分割
Lesion Net -- Skin Lesion Segmentation Using Coordinate Convolution and Deep Residual Units
论文作者
论文摘要
皮肤病变分割是皮肤黑色素瘤自动诊断过程中的重要一步。但是，由于训练，不规则形状，不明确的边界和不同的皮肤颜色的数据较少，因此分割黑色素瘤皮肤病变的准确性是一项艰巨的任务。我们提出的方法有助于提高皮肤病变细分的准确性。首先，在将输入图像传递到编码器中之前，我们已经引入了坐标卷积层。该层有助于网络决定与翻译不变性相关的功能，从而进一步提高了模型的概括能力。其次，我们利用了深残差单位和卷积层的性质。最后，我们结合了两个损失功能，而不是仅使用交叉渗透或骰子损失，以优化训练指标，这有助于更快，更平稳地收敛损失。在训练和验证ISIC 2018上的拟议模型（60％为火车集 + 20％作为验证集）之后，我们在其他各种数据集上测试了训练有素的模型的鲁棒性，例如ISIC 2018（20％作为测试集）ISIC 2017、2016和PH2数据集。结果表明，所提出的模型要么表现优于或与现有的皮肤病变分割方法相提并论。
Skin lesions segmentation is an important step in the process of automated diagnosis of the skin melanoma. However, the accuracy of segmenting melanomas skin lesions is quite a challenging task due to less data for training, irregular shapes, unclear boundaries, and different skin colors. Our proposed approach helps in improving the accuracy of skin lesion segmentation. Firstly, we have introduced the coordinate convolutional layer before passing the input image into the encoder. This layer helps the network to decide on the features related to translation invariance which further improves the generalization capacity of the model. Secondly, we have leveraged the properties of deep residual units along with the convolutional layers. At last, instead of using only cross-entropy or Dice-loss, we have combined the two-loss functions to optimize the training metrics which helps in converging the loss more quickly and smoothly. After training and validating the proposed model on ISIC 2018 (60% as train set + 20% as validation set), we tested the robustness of our trained model on various other datasets like ISIC 2018 (20% as test-set) ISIC 2017, 2016 and PH2 dataset. The results show that the proposed model either outperform or at par with the existing skin lesion segmentation methods.
