In microscopy image cell segmentation, it is common to train a deep neural network on source data, containing different types of microscopy images, and then fine-tune it using a support set comprising a few randomly selected and annotated training target images. In this paper, we argue that the random selection of unlabelled training target images to be annotated and included in the support set may not enable an effective fine-tuning process, so we propose a new approach to optimise this image selection process. Our approach involves a new scoring function to find informative unlabelled target images. In particular, we propose to measure the consistency in the model predictions on target images against specific data augmentations. However, we observe that the model trained with source datasets does not reliably evaluate consistency on target images. To alleviate this problem, we propose novel self-supervised pretext tasks to compute the scores of unlabelled target images. Finally, the top few images with the least consistency scores are added to the support set for oracle (i.e., expert) annotation and later used to fine-tune the model to the target images. In our evaluations that involve the segmentation of five different types of cell images, we demonstrate promising results on several target test sets compared to the random selection approach as well as other selection approaches, such as Shannon's entropy and Monte-Carlo dropout.
翻译:在显微镜图像单元格分解中,常见的做法是对源数据进行深层神经网络培训,包含不同类型的显微镜图像,然后使用由少数随机选择和附加说明的培训目标图像组成的支持集对其进行微调。在本文中,我们争辩说,随机选择未贴标签的培训目标图像以附加注释并纳入支持集可能无法实现有效的微调进程,因此我们建议了一种优化这种图像选择程序的新方法。我们的方法包括一个新的评分功能,以查找信息性、未贴标签的目标图像。特别是,我们提议用特定数据增强量来衡量目标图像模型预测的一致性。然而,我们观察到,经过源数据集培训的模型并不可靠地评估目标图像的一致性。为了缓解这一问题,我们提出了新的自我监督的托辞任务,以计算未贴标签目标图像的分数。最后,在Ocle(例如,专家)的评分中添加了很少的顶级功能,随后又用于对目标图像的模型与特定数据增强值进行微调。我们观察到,用源数据集所训练的模型并不可靠地评价目标图像的一致性。我们用不同的评选取方法来进行不同的评析。