ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization
This work addresses severity assessment in medical diagnosis, which is crucial for clinical decision-making, but it appears incremental as it builds on existing contrastive learning methods with a novel integration.
The paper tackled the problem of assessing severity in medical images by proposing ConPrO, a representation learning method that integrates contrastive learning with preference optimization to encode severity ordering, achieving a 6% improvement over supervised baselines and 20% over self-supervised ones.
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.