Golhar Mayank, Bobrow Taylor L, Khoshknab Mirmilad Pourmousavi, Jit Simran, Ngamruengphong Saowanee, Durr Nicholas J
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
IEEE Access. 2021;9:631-640. doi: 10.1109/access.2020.3047544. Epub 2020 Dec 25.
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.
虽然数据驱动的方法在许多图像分析任务中表现出色，但这些方法的性能往往受到训练可用标注数据短缺的限制。半监督学习的最新研究表明，可以通过使用大量未标注数据进行训练来获得有意义的图像表示，并且这些表示可以提高监督任务的性能。在这里，我们证明，与完全监督的基线相比，无监督拼图学习任务与监督训练相结合，在正确分类结肠镜检查图像中的病变方面可提高高达9.8%。我们还对域适应和分布外检测的改进进行了基准测试，并证明在这两种情况下半监督学习都优于监督学习。在结肠镜检查应用中，考虑到内镜病变评估所需的技能、使用的各种内镜系统以及标记数据集的典型同质性，这些指标很重要。
