论文标题
使用半监督深度学习改善结肠镜检查分类
Improving colonoscopy lesion classification using semi-supervised deep learning
论文作者
论文摘要
尽管数据驱动的方法在许多图像分析任务上都表现出色，但这些方法的性能通常受到可用于培训的注释数据的短缺。半监督学习中的最新工作表明，可以通过大量未标记的数据从培训中获得有意义的图像表示，并且这些表示形式可以改善监督任务的性能。在这里，我们证明了一项无监督的拼图学习任务与受监督的培训结合使用，与完全监督的基线相比，在结肠镜检查中正确分类的病变的正确分类为9.8％。另外，我们还基于域适应和分布外检测的基准改进，并证明了在这两种情况下，半监督学习的学习都超过了监督的学习。在结肠镜检查应用中，考虑到内窥镜评估病变所需的技能，使用的多种内窥镜系统以及典型的标记数据集的同质性所需的技能，这些指标很重要。
While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of unlabeled data, and that these representations can improve the performance of supervised tasks. Here, we demonstrate that an unsupervised jigsaw learning task, in combination with supervised training, results in up to a 9.8% improvement in correctly classifying lesions in colonoscopy images when compared to a fully-supervised baseline. We additionally benchmark improvements in domain adaptation and out-of-distribution detection, and demonstrate that semi-supervised learning outperforms supervised learning in both cases. In colonoscopy applications, these metrics are important given the skill required for endoscopic assessment of lesions, the wide variety of endoscopy systems in use, and the homogeneity that is typical of labeled datasets.
