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

arXiv:2404.18831v1 Announce Type: new
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 …

abstract arxiv assessment clinical cs.ai cs.cv diagnosis guide images key medical novel optimization paper representation representation learning treatment type understanding