基于半监督微调的代理标签选择的热启动主动学习
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning
摘要：
在构建用于深度学习的医学成像数据集时，下一步注释哪一卷是一个具有挑战性的问题。主动学习是解决这一问题的有效方法之一。然而，AL一直是一个很难破解的AL算法和采集功能是最有用的数据集。此外，当开始时没有标记的数据时，首先标记哪些卷会加剧问题。这被称为AL中的冷启动问题。我们提出了两个新的战略AL专门用于3D图像分割。首先，我们提出了一个代理任务，然后利用代理任务产生的不确定性排名的未标记的数据进行注释来解决冷启动问题。其次，我们为每个活动迭代制作了一个两阶段的学习框架，其中未标记的数据也用于第二阶段作为半监督微调策略。我们展示了我们的方法在两个著名的大型公共数据集上的承诺，这些数据集来自医疗分割十项全能。结果表明，初始数据的选择和半监督框架都表现出显着的改善几个AL策略。
英文摘要：
Which volume to annotate next is a challenging problem in building medical imaging datasets for deep learning. One of the promising methods to approach this question is active learning (AL). However, AL has been a hard nut to crack in terms of which AL algorithm and acquisition functions are most useful for which datasets. Also, the problem is exacerbated with which volumes to label first when there is zero labeled data to start with. This is known as the cold start problem in AL. We propose two novel strategies for AL specifically for 3D image segmentation. First, we tackle the cold start problem by proposing a proxy task and then utilizing uncertainty generated from the proxy task to rank the unlabeled data to be annotated. Second, we craft a two-stage learning framework for each active iteration where the unlabeled data is also used in the second stage as a semi-supervised fine-tuning strategy. We show the promise of our approach on two well-known large public datasets from medical segmentation decathlon. The results indicate that the initial selection of data and semi-supervised framework both showed significant improvement for several AL strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
