Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

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.

Automated summary

This paper proposes AdaCon, a contrastive learning framework for deep image regression, which incorporates a novel adaptive-margin contrastive loss and a regression prediction branch for feature learning. By considering label distance relationships in feature representations, AdaCon achieves better performance in downstream regression tasks. Experimental results on two medical image regression tasks demonstrate the effectiveness of AdaCon, with relative improvements of 3.3% and 5.9% in MAE compared to state-of-the-art methods for BMD estimation and LVEF prediction, respectively.