用合成数据训练显微镜分割模型，实现癌症早期检测的自动分析。
Synthetic-to-Real Transfer Learning for Chromatin-Sensitive PWS Microscopy
- 基于物理渲染生成合成多模态数据，分三阶段训练神经网络。
- 在合成数据上达Dice 0.9879，自动分割百万级细胞核。
- 支持低精度推理，提速240倍，适合临床大规模筛查。
染色质敏感部分波谱显微镜（csPWS）可无标记检测癌变前纳米尺度染色质构象变化，但人工核分割限制了大规模生物标志物发现。因缺乏标注数据，传统深度学习难以应用。本文提出CFU Net，一种基于三阶段课程学习的层次化分割架构，使用基于物理的合成多模态数据训练。该模型在未见合成测试数据上表现近乎完美（Dice 0.9879，IoU 0.9895），无需人工标注。方法融合经验性染色质统计、米氏散射模型与模态特异性噪声，课程设计从对抗性RGB预训练到光谱微调及组织学验证。整合ConvNeXt主干、特征金字塔、UNet++密集连接、双重注意力与深层监督五项结构，使Dice比基线UNet提升8.3%。部署采用INT8量化，压缩率达74.9%，单次推理仅需0.15秒，吞吐量较人工分析提升240倍。对超万例合成数据自动分割的核进行分析，提取的染色质生物标志物可显著区分正常与癌前组织（Cohen's d介于1.31至2.98），分类准确率达94%。本工作为特殊显微技术提供通用的合成到真实迁移学习框架，并开放资源供临床样本验证。
原文摘要 · Abstract (English)
Chromatin sensitive partial wave spectroscopic (csPWS) microscopy enables label free detection of nanoscale chromatin packing alterations that occur before visible cellular transformation. However, manual nuclear segmentation limits population scale analysis needed for biomarker discovery in early cancer detection. The lack of annotated csPWS imaging data prevents direct use of standard deep learning methods. We present CFU Net, a hierarchical segmentation architecture trained with a three stage curriculum on synthetic multimodal data. CFU Net achieves near perfect performance on held out synthetic test data that represent diverse spectroscopic imaging conditions without manual annotations (Dice 0.9879, IoU 0.9895). Our approach uses physics based rendering that incorporates empirically supported chromatin packing statistics, Mie scattering models, and modality specific noise, combined with a curriculum that progresses from adversarial RGB pretraining to spectroscopic fine tuning and histology validation. CFU Net integrates five architectural elements (ConvNeXt backbone, Feature Pyramid Network, UNet plus plus dense connections, dual attention, and deep supervision) that together improve Dice over a baseline UNet by 8.3 percent. We demonstrate deployment ready INT8 quantization with 74.9 percent compression and 0.15 second inference, giving a 240 times throughput gain over manual analysis. Applied to more than ten thousand automatically segmented nuclei from synthetic test data, the pipeline extracts chromatin biomarkers that distinguish normal from pre cancerous tissue with large effect sizes (Cohens d between 1.31 and 2.98), reaching 94 percent classification accuracy. This work provides a general framework for synthetic to real transfer learning in specialized microscopy and open resources for community validation on clinical specimens.
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