In this paper, we propose a novel selection strategy for contrastive learning for medical images. On natural images, contrastive learning uses augmentations to select positive and negative pairs for the contrastive loss. However, in the medical domain, arbitrary augmentations have the potential to distort small localized regions that contain the biomarkers we are interested in detecting. A more intuitive approach is to select samples with similar disease severity characteristics, since these samples are more likely to have similar structures related to the progression of a disease. To enable this, we introduce a method that generates disease severity labels for unlabeled OCT scans on the basis of gradient responses from an anomaly detection algorithm. These labels are used to train a supervised contrastive learning setup to improve biomarker classification accuracy by as much as 6% above self-supervised baselines for key indicators of Diabetic Retinopathy.

翻译：本文提出一种针对医学图像的对比学习新颖选择策略。在自然图像领域，对比学习通常采用数据增强技术来构建对比损失所需的正负样本对。然而在医学影像中，任意增强操作可能扭曲包含目标生物标志物的局部微小区域。更直观的方法是选择具有相似疾病严重程度特征的样本，因为这些样本更可能具有与疾病进展相关的相似结构。为此，我们提出一种基于异常检测算法梯度响应为未标注OCT扫描生成疾病严重程度标签的方法。这些标签用于训练监督对比学习框架，在糖尿病视网膜病变关键指标分类任务中，将生物标志物分类准确率较自监督基线方法提升达6%。