Self-supervised representation learning on image-text data facilitates
crucial medical applications, such as image classification, visual grounding,
and cross-modal retrieval. One common approach involves contrasting
semantically similar (positive) and dissimilar (negative) pairs of data points.
Drawing negative samples uniformly from the training data set introduces false
negatives, i.e., samples that are treated as dissimilar but belong to the same
class. In healthcare data, the underlying class distribution is nonuniform,
implying that false negatives occur at a highly variable rate. To improve the
quality of learned representations, we develop a novel approach that corrects
for false negatives. Our method can be viewed as a variant of debiased
constrastive learning that uses estimated sample-specific class probabilities.
We provide theoretical analysis of the objective function and demonstrate the
proposed approach on both image and paired image-text data sets. Our
experiments demonstrate empirical advantages of sample-specific debiasing.

本文介绍了一种基于样本特定偏差校正的自监督学习方法，用于改善不均匀假负面样本导致的图像文本数据的质量问题。经过实验证明，此方法在包括医学应用在内的多种数据集上都取得了比传统方法更好的效果。