用扩散模型将偏振成像转为标准染色图，提升病理诊断清晰度。
Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging
- 设计双向桥接扩散模型，学习偏振图到染色图的映射关系。
- 在1.8万对数据上实验，生成图像质量显著优于现有方法。
- 适用于病理图像分析，尤其适合缺乏公开数据的科研团队。
偏振成像作为一种新型光学诊断工具，有助于病理分析。将偏振的穆勒矩阵（MM）转换为标准化染色图像，是帮助病理医生解读结果的有前景方法。然而，当前基于偏振的虚拟染色技术仍处于早期阶段，而已在图像生成中展现高保真潜力的扩散模型尚未被探索。本文提出一种受控桥接扩散模型（RBDM），通过双向桥接扩散过程，学习从偏振图像到H&E和荧光图像等模态的映射。为验证模型有效性，我们在手动收集的18,000对偏振、荧光和H&E图像数据集上进行实验，因缺乏公开数据集。实验结果表明，该模型显著优于其他基准方法。论文接受后将公开数据集与代码。
原文摘要 · Abstract (English)
Polarization, as a new optical imaging tool, has been explored to assist in the diagnosis of pathology. Moreover, converting the polarimetric Mueller Matrix (MM) to standardized stained images becomes a promising approach to help pathologists interpret the results. However, existing methods for polarization-based virtual staining are still in the early stage, and the diffusion-based model, which has shown great potential in enhancing the fidelity of the generated images, has not been studied yet. In this paper, a Regulated Bridge Diffusion Model (RBDM) for polarization-based virtual staining is proposed. RBDM utilizes the bidirectional bridge diffusion process to learn the mapping from polarization images to other modalities such as H\&E and fluorescence. And to demonstrate the effectiveness of our model, we conduct the experiment on our manually collected dataset, which consists of 18,000 paired polarization, fluorescence and H\&E images, due to the unavailability of the public dataset. The experiment results show that our model greatly outperforms other benchmark methods. Our dataset and code will be released upon acceptance.
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