论文标题
Matthews的相关系数损失的深卷积网络：应用于皮肤病变分段
Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation
论文作者
论文摘要
皮肤病变的分割是计算机诊断皮肤病变的临床决策支持系统中的至关重要任务。尽管基于深度学习的方法提高了细分性能，但这些模型通常容易受到数据中阶级失衡的影响，尤其是背景健康皮肤所占据的图像的比例。尽管提出了解决阶级失衡问题的流行骰子损失功能的变化，但骰子损失配方并没有惩罚对背景像素的错误分类。我们建议使用Matthews相关系数的新型基于度量的损耗函数，该度量已被证明在具有偏斜的类别分布的情况下有效，并使用它来优化深层分割模型。对三个皮肤病变图像数据集的评估：ISBI ISIC 2017皮肤病变细分挑战数据集，Dermofit Image库和PH2数据集，表明，使用拟议损失功能培训的模型超过了使用骰子损失的培训的模型优于11.25％，4.87％，4.87％和0.76％的模型。该代码可从https://github.com/kakumarabhishek/mcc-loss获得。
The segmentation of skin lesions is a crucial task in clinical decision support systems for the computer aided diagnosis of skin lesions. Although deep learning-based approaches have improved segmentation performance, these models are often susceptible to class imbalance in the data, particularly, the fraction of the image occupied by the background healthy skin. Despite variations of the popular Dice loss function being proposed to tackle the class imbalance problem, the Dice loss formulation does not penalize misclassifications of the background pixels. We propose a novel metric-based loss function using the Matthews correlation coefficient, a metric that has been shown to be efficient in scenarios with skewed class distributions, and use it to optimize deep segmentation models. Evaluations on three skin lesion image datasets: the ISBI ISIC 2017 Skin Lesion Segmentation Challenge dataset, the DermoFit Image Library, and the PH2 dataset, show that models trained using the proposed loss function outperform those trained using Dice loss by 11.25%, 4.87%, and 0.76% respectively in the mean Jaccard index. The code is available at https://github.com/kakumarabhishek/MCC-Loss.
