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。

基于多层次关注力交叉蒸馏的半监督病理图像分割方法

Semi-supervised Pathological Image Segmentation via Cross Distillation  of Multiple Attentions

An approach by which to analyze the performance of the code division multiple
access (CDMA) scheme, which is a core technology used in modern wireless
communication systems, is provided. The approach characterizes the objective
system by the eigenvalue spectrum of a cross-correlation matrix composed of
signature sequences used in CDMA communication, which enables us to handle a
wider class of CDMA systems beyond the basic model reported by Tanaka. The
utility of the novel scheme is shown by analyzing a system in which the
generation of signature sequences is designed for enhancing the orthogonality.

利用特征值谱分析交叉相关矩阵来评估 CDMA 方案的性能，从而构建具有更广泛适用性的 CDMA 系统，其应用为通过生成具有正交性的标志序列来增强 CDMA 系统的性能。