IEEE Trans Med Imaging. 2022 May;41(5):1255-1268. doi: 10.1109/TMI.2021.3137854. Epub 2022 May 2.
Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, i.e., bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.
用于医学应用的图像回归任务，如骨密度 (BMD) 估计和左心室射血分数 (LVEF) 预测，在计算机辅助疾病评估中起着重要作用。大多数深度回归方法使用单一回归损失函数（如均方误差或 L1 损失）来训练神经网络。在本文中，我们提出了第一个深度图像回归的对比学习框架，即 AdaCon，它由一个通过新颖的自适应边缘对比损失的特征学习分支和一个回归预测分支组成。我们的方法将标签距离关系作为学习特征表示的一部分，这使得在下游回归任务中具有更好的性能。此外，它可以用作插件和播放模块，以提高现有回归方法的性能。我们在两个医学图像回归任务上证明了 AdaCon 的有效性，即从 X 射线图像估计骨密度和从超声心动图视频预测左心室射血分数。AdaCon 在 MAE 上分别比最先进的 BMD 估计和 LVEF 预测方法提高了 3.3%和 5.9%。
