Segmentation of pathological images is a crucial step for accurate cancer diagnosis. However, acquiring dense annotations of such images for training is labor-intensive and time-consuming. To address this issue, Semi-Supervised Learning (SSL) has the potential for reducing the annotation cost, but it is challenged by a large number of unlabeled training images. In this paper, we propose a novel SSL method based on Cross Distillation of Multiple Attentions (CDMA) to effectively leverage unlabeled images. Firstly, we propose a Multi-attention Tri-branch Network (MTNet) that consists of an encoder and a three-branch decoder, with each branch using a different attention mechanism that calibrates features in different aspects to generate diverse outputs. Secondly, we introduce Cross Decoder Knowledge Distillation (CDKD) between the three decoder branches, allowing them to learn from each other's soft labels to mitigate the negative impact of incorrect pseudo labels in training. Additionally, uncertainty minimization is applied to the average prediction of the three branches, which further regularizes predictions on unlabeled images and encourages inter-branch consistency. Our proposed CDMA was compared with eight state-of-the-art SSL methods on the public DigestPath dataset, and the experimental results showed that our method outperforms the other approaches under different annotation ratios. The code is available at \href{https://github.com/HiLab-git/CDMA}{https://github.com/HiLab-git/CDMA.}

翻译：病理图像分割是准确诊断癌症的关键步骤。然而，为训练获取此类图像的密集标注既耗时又费力。为解决这一问题，半监督学习（SSL）具有降低标注成本的潜力，但因大量未标注训练图像的存在而面临挑战。本文提出一种基于多重注意力交叉蒸馏（CDMA）的新型半监督学习方法，以有效利用未标注图像。首先，我们提出一种多注意力三支路网络（MTNet），它由一个编码器和三支路解码器组成，每条支路采用不同的注意力机制，从不同方面校准特征以生成多样化输出。其次，我们在三个解码器支路之间引入交叉解码器知识蒸馏（CDKD），使其能够相互学习彼此的软标签，以减轻训练中错误伪标签的负面影响。此外，对三条支路的平均预测进行不确定性最小化处理，进一步规范对未标注图像的预测，并促进支路间一致性。在公开DigestPath数据集上，我们将所提出的CDMA与八种最新的SSL方法进行比较，实验结果表明，在不同标注比例下，我们的方法均优于其他方法。代码可在\href{https://github.com/HiLab-git/CDMA}{https://github.com/HiLab-git/CDMA}获取。