减轻标注负担：通过时空一致性再学习利用图像知识实现少样本医学视频目标分割
Reducing Annotation Burden: Exploiting Image Knowledge for Few-Shot Medical Video Object Segmentation via Spatiotemporal Consistency Relearning
AI总结：
针对医学视频标注稀缺问题，提出两阶段框架，先利用标注图像训练少样本分割模型，再通过时空一致性再学习及特征、预测层约束提升性能，在低数据场景下实现优于现有方法的医学视频分割效果。
摘要：
少样本视频目标分割旨在降低标注成本；然而，现有方法仍需要大量密集帧标注用于训练，而这类标注在医学领域十分稀缺。我们研究了一种极低数据量的场景：仅使用少量视频帧的标注，并利用已有的标注图像来减少成本高昂的视频标注需求。具体而言，我们提出一个两阶段框架。首先，利用标注图像训练一个少样本分割模型。随后，为在无全监督的条件下提升性能，我们在医学视频上引入时空一致性再学习方法，强制连续帧之间保持一致性。同时在特征层和预测层对图像模型与再学习模型施加约束。实验表明，我们的方法优于当前最优的少样本分割方法。该模型填补了标注充足的医学图像与标注稀缺且稀疏的医学视频之间的差距，在这种低数据量场景下实现了出色的视频分割性能。代码可在https://github.com/MedAITech/RAB获取。
英文摘要：
Few-shot video object segmentation aims to reduce annotation costs; however, existing methods still require abundant dense frame annotations for training, which are scarce in the medical domain. We investigate an extremely low-data regime that utilizes annotations from only a few video frames and leverages existing labeled images to minimize costly video annotations. Specifically, we propose a two-phase framework. First, we learn a few-shot segmentation model using labeled images. Subsequently, to improve performance without full supervision, we introduce a spatiotemporal consistency relearning approach on medical videos that enforces consistency between consecutive frames. Constraints are also enforced between the image model and relearning model at both feature and prediction levels. Experiments demonstrate the superiority of our approach over state-of-the-art few-shot segmentation methods. Our model bridges the gap between abundant annotated medical images and scarce, sparsely labeled medical videos to achieve strong video segmentation performance in this low data regime. Code is available at https://github.com/MedAITech/RAB.
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。
