论文标题
回归度量损失：学习医学图像的语义表示空间
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images
论文作者
论文摘要
回归在估计各种临床风险或测量评分的许多医学成像应用中起着至关重要的作用。尽管已经研究了医学图像分类任务中深层神经网络的培训策略和损失功能，但回归任务的选项非常有限。关键挑战之一是，很难解释由现有流行损失函数（如平方误差或L1损失）所学的高维特征表示。在本文中，我们提出了一种新颖的回归度量损失（RM-loss），该损失通过找到标签空间等均衡的表示歧管来赋予表示空间的语义含义。对两个回归任务的实验，即冠状动脉钙评分估计和骨龄评估，表明RM-LOSS优于在性能和可解释性上的现有流行回归损失。代码可在https://github.com/dial-rpi/regression-metric-loss上找到。
Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions have been studied for the deep neural networks in medical image classification tasks, options for regression tasks are very limited. One of the key challenges is that the high-dimensional feature representation learned by existing popular loss functions like Mean Squared Error or L1 loss is hard to interpret. In this paper, we propose a novel Regression Metric Loss (RM-Loss), which endows the representation space with the semantic meaning of the label space by finding a representation manifold that is isometric to the label space. Experiments on two regression tasks, i.e. coronary artery calcium score estimation and bone age assessment, show that RM-Loss is superior to the existing popular regression losses on both performance and interpretability. Code is available at https://github.com/DIAL-RPI/Regression-Metric-Loss.
