# Spurious Invariance

Medical self-supervised contrastive learning assumes two augmented views of the same image should share one semantic representation, but rotation and intensity perturbations can erase subtle yet critical anatomical and pathological cues, so rigid alignment suppresses task-relevant information. ProbSSL models each image embedding as a Gaussian, with the mean carrying shared semantics and the variance recording augmentation-induced variability, and aligns distributions by KL divergence so dimensions with larger variability receive weaker alignment pressure. After pre-training on CT, transfer to BTCV, MM-WHS, CC-CCII and BraTS gives higher average scores than the compared general and medical self-supervised methods on all four downstream tasks, including 84.89 mean Dice on BTCV.

Medical self-supervised contrastive learning assumes two augmented views of the same image should share one semantic representation, but rotation and intensity perturbations can erase subtle yet critical anatomical and pathological cues, so rigid alignment suppresses task-relevant information. ProbSSL models each image embedding as a Gaussian, with the mean carrying shared semantics and the variance recording augmentation-induced variability, and aligns distributions by KL divergence so dimensions with larger variability receive weaker alignment pressure. After pre-training on CT, transfer to BTCV, MM-WHS, CC-CCII and BraTS gives higher average scores than the compared general and medical self-supervised methods on all four downstream tasks, including 84.89 mean Dice on BTCV.