论文标题
损失功能在深度学习方法中的影响，以进行精确的视网膜船分割
Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation
论文作者
论文摘要
通过眼底图像研究的视网膜血管网络不仅有助于诊断多种疾病。该系统的分割可以通过协助量化形态特征来帮助分析这些图像的专业任务。由于其相关性，已经测试过几种基于学习的深度架构，以自动解决此问题。但是，尚未系统地评估损失函数选择对复杂的视网膜血管系统分割的影响。在这项工作中，我们使用深度学习体系结构（即U-NET，注意U-NET和嵌套的UNET）与驱动器数据集进行了损失函数二进制交叉熵，骰子，Tversky和组合损失的比较。使用四个指标评估它们的性能：AUC，平方误差，骰子得分和Hausdorff距离。这些模型经过相同数量的参数和时期训练。使用骰子得分和AUC，最佳组合是SA-UNET，组合损失分别为0.9442和0.809。使用嵌套的U-NET获得了Hausdorff距离和均方根误差的最佳平均值，其骰子损失函数平均为6.32和0.0241。结果表明，损失函数的选择存在显着差异
The retinal vessel network studied through fundus images contributes to the diagnosis of multiple diseases not only found in the eye. The segmentation of this system may help the specialized task of analyzing these images by assisting in the quantification of morphological characteristics. Due to its relevance, several Deep Learning-based architectures have been tested for tackling this problem automatically. However, the impact of loss function selection on the segmentation of the intricate retinal blood vessel system hasn't been systematically evaluated. In this work, we present the comparison of the loss functions Binary Cross Entropy, Dice, Tversky, and Combo loss using the deep learning architectures (i.e. U-Net, Attention U-Net, and Nested UNet) with the DRIVE dataset. Their performance is assessed using four metrics: the AUC, the mean squared error, the dice score, and the Hausdorff distance. The models were trained with the same number of parameters and epochs. Using dice score and AUC, the best combination was SA-UNet with Combo loss, which had an average of 0.9442 and 0.809 respectively. The best average of Hausdorff distance and mean square error were obtained using the Nested U-Net with the Dice loss function, which had an average of 6.32 and 0.0241 respectively. The results showed that there is a significant difference in the selection of loss function
