Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study introduces a novel deep learning-based framework for virtual histological staining using photon absorption remote sensing (PARS) images. By extracting features from PARS time-resolved signals using a variant of the K-means method, valuable multi-modal information is captured. The proposed multi-channel cycleGAN model expands on the traditional cycleGAN framework, allowing the inclusion of additional features. Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training. Applied to human skin and mouse brain tissue, the results underscore the significance of choosing the optimal combination of features, as it reveals a substantial visual and quantitative concurrence between the virtually stained and the gold standard chemically stained hematoxylin and eosin images, surpassing the performance of other feature combinations. Accurate virtual staining is valuable for reliable diagnostic information, aiding pathologists in disease classification, grading, and treatment planning. This study aims to advance label-free histological imaging and opens doors for intraoperative microscopy applications.

准确、快速的组织学染色在组织病理学中至关重要，会影响诊断的精确度和可靠性。传统的染色方法既耗时又主观，会导致诊断延误。数字病理学在推进和优化组织学流程以提高效率和缩短周转时间方面发挥着至关重要的作用。本研究介绍了一种基于深度学习的新型框架，用于使用光子吸收遥感 （PARS） 图像进行虚拟组织学染色。通过使用 K-means 方法的变体从 PARS 时间分辨信号中提取特征，可以捕获有价值的多模态信息。提出的多通道 cycleGAN 模型扩展了传统的 cycleGAN 框架，允许包含额外的功能。实验结果表明，通过在模型训练之前改善组织结构的标记，特定的特征组合优于传统通道。应用于人类皮肤和小鼠脑组织，结果强调了选择最佳特征组合的重要性，因为它揭示了虚拟染色和金标准化学染色的苏木精和伊红图像之间的视觉和定量一致性，超过了其他特征组合的性能。准确的虚拟染色对于可靠的诊断信息非常有价值，可帮助病理学家进行疾病分类、分级和治疗计划。本研究旨在推进无标记组织学成像，并为术中显微镜应用打开大门。