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In medical image analysis, regression plays a critical role in computer-aideddiagnosis. It enables quantitative measurements such as age prediction fromstructural imaging, cardiac function quantification, and molecular measurementfrom PET scans. While deep learning has shown promise for these tasks, mostapproaches focus solely on optimizing regression loss or model architecture,neglecting the quality of learned feature representations which are crucial forrobust clinical predictions. Directly applying representation learningtechniques designed for classification to regression often results infragmented representations in the latent space, yielding sub-optimalperformance. In this paper, we argue that the potential of contrastive learningfor medical image regression has been overshadowed due to the neglect of twocrucial aspects: ordinality-awareness and hardness. To address thesechallenges, we propose Supervised Contrastive Learning for Medical ImagingRegression with Mixup (SupReMix). It takes anchor-inclusive mixtures (mixup ofthe anchor and a distinct negative sample) as hard negative pairs andanchor-exclusive mixtures (mixup of two distinct negative samples) as hardpositive pairs at the embedding level. This strategy formulates hardercontrastive pairs by integrating richer ordinal information. Throughtheoretical analysis and extensive experiments on six datasets spanning MRI,X-ray, ultrasound, and PET modalities, we demonstrate that SupReMix fosterscontinuous ordered representations, significantly improving regressionperformance.

SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixup