论文标题
匹配临床现实：从少数标签中获得准确的基于OCT的诊断
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
论文作者
论文摘要
在诊所中，未标记的数据通常很丰富，这使基于半监督学习的机器学习方法可以很好地匹配此设置。尽管如此，他们目前在医学图像分析文献中受到相对较少的关注。相反，大多数从业者和研究人员都专注于受监督或转移学习方法。最近提出的混合截图和FixMatch算法在提取有用的表示方面表现出了有希望的结果，同时需要很少的标签。在最近的成功中，我们在眼科诊断环境中应用混合匹配和FixMatch，并调查它们如何抵制标准转移学习。我们发现，这两种算法的表现都优于标记数据的所有分数的传输学习基线。此外，我们的实验表明，模型参数的指数移动平均值（EMA）是两种算法的组成部分，我们的分类问题不需要，因为禁用结果使结果不变。我们的代码可在线提供：https：//github.com/valentyn1997/oct-diagn-semi-superishis
Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently receiving relatively little attention in medical image analysis literature. Instead, most practitioners and researchers focus on supervised or transfer learning approaches. The recently proposed MixMatch and FixMatch algorithms have demonstrated promising results in extracting useful representations while requiring very few labels. Motivated by these recent successes, we apply MixMatch and FixMatch in an ophthalmological diagnostic setting and investigate how they fare against standard transfer learning. We find that both algorithms outperform the transfer learning baseline on all fractions of labelled data. Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged. Our code is available online: https://github.com/Valentyn1997/oct-diagn-semi-supervised
