用梯度响应生成病灶严重程度标签，提升OCT图像生物标志物分类准确率。
Gradient based Severity Labeling for Biomarker Classification in OCT
- 基于异常检测算法的梯度响应生成未标注OCT图像的严重程度标签
- 在糖尿病视网膜病变关键指标上比自监督基线提升6%分类准确率
- 适用于需精准识别微小病灶的医学图像分析任务
本文提出一种针对医学图像对比学习的新颖样本选择策略。自然图像中，对比学习通过数据增强生成正负样本对；但在医学领域，任意增强可能扭曲包含生物标志物的微小局部区域。更合理的做法是选取疾病严重程度相似的样本，因其更可能具有与疾病进展相关的相似结构。为此，我们提出一种方法，基于异常检测算法的梯度响应为未标注OCT扫描生成疾病严重程度标签。这些标签用于训练监督式对比学习模型，在糖尿病视网膜病变的关键指标上，分类准确率相较自监督基线最高提升6%。
原文摘要 · Abstract (English)
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.
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