用自监督学习生成乳腺癌图像，提升手术中边缘判读准确率
Self-learned representation-guided latent diffusion model for breast cancer classification in deep ultraviolet whole surface images
- 用自监督模型生成带细胞结构的合成病理图像
- 96.47%分类准确率，生成图像质量FID仅45.72
- 适合缺乏标注数据的医学图像分析场景
保乳手术需要精确的术中边缘评估以保留健康组织。深紫外荧光扫描显微镜（DUV-FSM）可快速获取高分辨率表面图像，但标注数据稀缺制约了深度学习模型训练。为此，我们提出一种自监督学习引导的潜在扩散模型（LDM），用于生成高质量合成训练样本。通过使用微调后的DINO教师模型提取的嵌入指导扩散过程，将丰富的细胞结构语义信息注入合成数据。结合真实与合成图像块，对视觉变换器（ViT）进行微调，并采用图像块预测聚合实现全切片（WSI）级分类。5折交叉验证实验表明，该方法达到96.47%的准确率，且生成图像的FID分数降至45.72，显著优于条件生成基线。
原文摘要 · Abstract (English)
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.
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