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}{this https URL}

本研究提出了一种基于多个注意力互相关联的交叉蒸馏的半监督学习方法，称为交叉多个注意力半监督学习 (CDMA)，应用于病理图像的分割中，该方法使用一个多重注意力三支网络 (MTNet) 和交叉解码器知识蒸馏 (CDKD)。实验研究表明，该方法在不同注释比率下优于其他八种现有的半监督学习方法。