用超图对比学习提升多域染色迁移，保留病理细节
Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer
- 基于超图建模图像块间高阶关系，保持输入输出拓扑一致
- 引入双高斯分布加权策略，提升负样本筛选效果
- 在染色迁移与下游任务中均表现领先，适合医学图像分析
虚拟染色迁移利用计算机技术将组织样本的染色模式转换为其他染色类型。然而，现有方法因循环一致性假设的局限性，常丢失细节病理信息。为此，我们提出STNHCL，一种基于超图的分块对比学习方法。STNHCL通过超图建模捕捉图像块间的高阶关系，确保输入与输出图像间保持一致的高阶拓扑结构。同时，我们引入一种新颖的负样本加权策略，利用判别器热图对组织和背景区域分别采用高斯分布加权，从而改进传统加权方式。实验表明，STNHCL在两大类染色迁移任务中达到当前最优性能，且在下游任务中也表现优异。代码已公开于https://github.com/Whywwwzzzg/STNHCL。
原文摘要 · Abstract (English)
Virtual stain transfer leverages computer-assisted technology to transform the histochemical staining patterns of tissue samples into other staining types. However, existing methods often lose detailed pathological information due to the limitations of the cycle consistency assumption. To address this challenge, we propose STNHCL, a hypergraph-based patch-wise contrastive learning method. STNHCL captures higher-order relationships among patches through hypergraph modeling, ensuring consistent higher-order topology between input and output images. Additionally, we introduce a novel negative sample weighting strategy that leverages discriminator heatmaps to apply different weights based on the Gaussian distribution for tissue and background, thereby enhancing traditional weighting methods. Experiments demonstrate that STNHCL achieves state-of-the-art performance in the two main categories of stain transfer tasks. Furthermore, our model also performs excellently in downstream tasks. Code is available at https://github.com/Whywwwzzzg/STNHCL
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