  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, ie, 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.
