Breast-Conserving Surgery (BCS) requires precise intraoperative margin assessment to preserve healthy tissue. Deep Ultraviolet Fluorescence Scanning Microscopy (DUV-FSM) offers rapid, high-resolution surface imaging for this purpose; however, the scarcity of annotated DUV data hinders the training of robust deep learning models. To address this, we propose an Self-Supervised Learning (SSL)-guided Latent Diffusion Model (LDM) to generate high-quality synthetic training patches. By guiding the LDM with embeddings from a fine-tuned DINO teacher, we inject rich semantic details of cellular structures into the synthetic data. We combine real and synthetic patches to fine-tune a Vision Transformer (ViT), utilizing patch prediction aggregation for WSI-level classification. Experiments using 5-fold cross-validation demonstrate that our method achieves 96.47 % accuracy and reduces the FID score to 45.72, significantly outperforming class-conditioned baselines.

翻译：保乳手术（BCS）需要精确的术中切缘评估以保留健康组织。为此，深紫外荧光扫描显微镜（DUV-FSM）提供了快速、高分辨率的表面成像；然而，标注DUV数据的稀缺性阻碍了鲁棒深度学习模型的训练。为解决这一问题，我们提出了一种自监督学习（SSL）引导的潜在扩散模型（LDM），用于生成高质量的合成训练图像块。通过使用来自微调DINO教师的嵌入向量引导LDM，我们将细胞结构的丰富语义细节注入合成数据中。我们结合真实与合成图像块来微调视觉Transformer（ViT），并利用图像块预测聚合进行全切片图像（WSI）级别的分类。使用五折交叉验证的实验表明，我们的方法达到了96.47%的准确率，并将FID分数降低至45.72，显著优于类别条件基线。