Structured Illumination Microscopy (SIM) enables rapid, high-contrast optical sectioning of fresh tissue without staining or physical sectioning, making it promising for intraoperative and point-of-care diagnostics. Recent foundation and large-scale self-supervised models in digital pathology have demonstrated strong performance on section-based modalities such as Hematoxylin and Eosin (H&E) and immunohistochemistry (IHC). However, these approaches are predominantly trained on thin tissue sections and do not explicitly address thick-tissue fluorescence modalities such as SIM. When transferred directly to SIM, performance is constrained by substantial modality shift, and naive fine-tuning often overfits to modality-specific appearance rather than underlying histological structure. We introduce SIMPLER (Structured Illumination Microscopy-Powered Learning for Embedding Representations), a cross-modality self-supervised pretraining framework that leverages H&E as a semantic anchor to learn reusable SIM representations. H&E encodes rich cellular and glandular structure aligned with established clinical annotations, while SIM provides rapid, nondestructive imaging of fresh tissue. During pretraining, SIM and H&E are progressively aligned through adversarial, contrastive, and reconstruction-based objectives, encouraging SIM embeddings to internalize histological structure from H&E without collapsing modality-specific characteristics. A single pretrained SIMPLER encoder transfers across multiple downstream tasks, including multiple instance learning and morphological clustering, consistently outperforming SIM models trained from scratch or H&E-only pretraining. These results suggest that histology-guided cross-modal pretraining yields biologically grounded SIM embeddings suitable for broad downstream reuse.

翻译：结构化照明显微镜（SIM）能够在不进行染色或物理切片的情况下，对新鲜组织实现快速、高对比度的光学切片，使其在术中及即时诊断中具有应用前景。近期数字病理学中的基础模型与大规模自监督模型在基于切片的模态（如苏木精-伊红染色（H&E）和免疫组织化学（IHC））上展现了强大性能。然而，这些方法主要基于薄组织切片训练，并未明确针对厚组织荧光成像模态（如SIM）。当直接迁移至SIM时，其性能会受到显著模态差异的制约，而朴素微调往往过拟合于模态特异性外观而非底层组织学结构。我们提出SIMPLER（结构化照明显微镜驱动的嵌入表示学习框架），这是一种跨模态自监督预训练框架，利用H&E作为语义锚点学习可复用的SIM表示。H&E编码了与临床标注一致的丰富细胞及腺体结构，而SIM则能对新鲜组织进行快速无创成像。在预训练过程中，通过对抗性、对比性和基于重建的损失函数逐步对齐SIM与H&E，促使SIM嵌入在保留模态特异性特征的前提下，从H&E中内化组织学结构。单一预训练的SIMPLER编码器可迁移至包含多实例学习与形态聚类在内的多个下游任务，其性能始终优于从零训练或仅基于H&E预训练的SIM模型。这些结果表明，组织学引导的跨模态预训练能够生成具有生物学基础的SIM嵌入，适用于广泛的下游复用场景。