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 Con- PrO: a novel representation learning method for severity assessment in medical images using Contrastive learningintegrated 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.

翻译：理解医学诊断中图像所呈现病情的严重程度至关重要，它是临床评估、治疗以及纵向进展评估的关键指导。本文提出ConPrO：一种结合对比学习与偏好优化的医学图像严重程度表征学习新方法。与最大化类别间距离的传统对比学习方法不同，ConPrO将不同严重程度类别与正常类别之间的距离偏好知识注入潜在向量中。我们系统性地研究了框架的关键组成部分，以阐明对比预测任务如何获取有价值的表征。研究表明，我们的表征学习框架在特征空间中提供了有效的严重程度排序，同时在分类任务上超越了先前最先进的方法。相比监督基线和自监督基线，我们分别实现了6%和20%的相对提升。此外，我们还推导了关于严重程度指标的讨论以及偏好比较在医学领域中的相关应用。