Abstract
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.
Abstract (translated)
理解医学图像中显示病情的严重程度对于医疗诊断至关重要，作为临床评估、治疗以及评估病程进展的关键指导。本文提出了一种名为Con-PrO的新的图像严重程度评估方法，该方法使用对比学习集成偏好优化。与传统的对比学习方法不同，ConPrO将各种严重程度类之间的距离偏好知识注入到潜在向量中。我们系统地检查我们框架的关键组件，以阐明对比预测任务如何获得有价值的表示。我们证明了，与以前的最先进方法相比，我们的表示学习框架在分类任务上实现了6%和20%的相对改进。此外，我们讨论了病理性指标及其在医学领域中的相关应用。
URL
https://arxiv.org/abs/2404.18831
