ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization

Hong Nguyen, Hoang Nguyen, Melinda Chang, Hieu Pham, Shrikanth Narayanan, Michael Pazzani; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5105-5112

Understanding the severity of conditions shown in images in medical diagnosis is crucial serving as a key guide for clinical assessment treatment as well as evaluating longitudinal progression. This paper proposes ConPrO: a novel representation learning method for severity assessment in medical images using Contrastive learning integrated Preference Optimization. Different from conventional contrastive learning methods that maximize the distance between classes ConPrO injects into the latent vector the distance preference knowledge between various severity classes and the normal class. We systematically examine the key components of our framework to illuminate how contrastive prediction tasks acquire valuable representations. We show that our representation learning framework offers valuable severity ordering in the feature space while outperforming previous state-of-the-art methods on classification tasks. We achieve a 6% and 20% relative improvement compared to a supervised and a self-supervised baseline respectively. In addition we derived discussions on severity indicators and related applications of preference comparison in the medical domain.