Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning.
Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning.
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DOI：
10.1109/access.2020.3047544
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Durr NJ
中科院分区：
文献类型：
--
作者：
Golhar M;Bobrow TL;Khoshknab MP;Jit S;Ngamruengphong S;Durr NJ
虽然数据驱动的方法在许多图像分析任务中表现出色，但这些方法的性能通常受到可用于训练的注释数据短缺的限制。最近在半监督学习方面的研究表明，可以从大量未标记数据的训练中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，我们证明了一个无监督的拼图学习任务，结合监督训练，与完全监督的基线相比，在正确分类结肠镜图像中的病变方面提高了9.8%。此外，我们还对域适应和分布外检测的改进进行了基准测试，并证明了半监督学习在这两种情况下都优于监督学习。在结肠镜检查应用中，考虑到病变的内窥镜评估所需的技能、使用中的各种内窥镜检查系统以及标记数据集的典型同质性，这些度量是重要的。
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.
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