C$^3$PS：面向半监督医学图像分割的上下文感知条件交叉伪监督
C$^3$PS: Context-aware Conditional Cross Pseudo Supervision for Semi-supervised Medical Image Segmentation
AI总结：
针对半监督医学图像分割，提出上下文感知条件交叉伪监督方法C$^3$PS，通过带类别条件的交叉伪监督结合上下文感知提升伪标签质量，训练后期可聚焦难分割器官，在两项任务上性能优于现有最优方法。
摘要：
半监督学习（SSL）方法可利用大量未标记数据提升性能，近年来受到越来越多的关注。本文提出一种面向半监督医学图像分割的新型上下文感知条件交叉伪监督方法（简称C$^3$PS）。与已有的交叉伪监督（CPS）研究不同，本文提出了一种新型条件交叉伪监督（CCPS）机制，其交叉伪监督以给定类别标签为条件。我们进一步在CCPS中引入上下文感知机制，以提升用于交叉伪监督的伪标签质量。该方法还具备另一优势：在训练后期可聚焦于难分割器官的学习。在两项典型且具挑战性的医学图像分割任务上的验证结果表明，本文方法的性能优于现有最优方法。
英文摘要：
Semi-supervised learning (SSL) methods, which can leverage a large amount of unlabeled data for improved performance, has attracted increasing attention recently. In this paper, we introduce a novel Context-aware Conditional Cross Pseudo Supervision method (referred as C$^3$PS) for semi-supervised medical image segmentation. Unlike previously published Cross Pseudo Supervision (CPS) works, this paper introduces a novel Conditional Cross Pseudo Supervision (CCPS) mechanism where the cross pseudo supervision is conditioned on a given class label. Context-awareness is further introduced in the CCPS to improve the quality of pseudo-labels for cross pseudo supervision. The proposed method has the additional advantage that in the later training stage, it can focus on the learning of hard organs. Validated on two typical yet challenging medical image segmentation tasks, our method demonstrates superior performance over the state-of-the-art methods.
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。
