用流匹配模型从组织切片图生成空间转录组，提升精度与效率。
Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching
- 通过建模整张切片基因表达联合分布，捕捉细胞间相互作用。
- 在HEST-1k和STImage-1K4M上相对基线提升超18%。
- 采用滑动窗口注意力编码器，支持超万点数据高效处理。
空间转录组学（ST）作为连接组织学成像与基因表达谱的强大技术，受限于低通量及专用实验设备。此前工作尝试从全切片组织图像预测ST以加速流程，但存在两大缺陷：其一，未显式建模细胞间相互作用，将整个切片的基因表达联合分布因子化，独立预测每个点的表达；其二，编码器因单张切片通常含超10,000个点而面临内存瓶颈。本文提出STFlow，一种基于流匹配的生成模型，通过建模整张切片的基因表达联合分布，显式捕捉细胞间交互。同时引入高效的滑动窗级编码器与局部空间注意力机制，实现全切片处理且无显著内存开销。在最新构建的HEST-1k与STImage-1K4M基准测试中，STFlow显著优于现有先进方法，相较病理基础模型实现超过18%的相对性能提升。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) has emerged as a powerful technology for bridging histology imaging with gene expression profiling. However, its application has been limited by low throughput and the need for specialized experimental facilities. Prior works sought to predict ST from whole-slide histology images to accelerate this process, but they suffer from two major limitations. First, they do not explicitly model cell-cell interaction as they factorize the joint distribution of whole-slide ST data and predict the gene expression of each spot independently. Second, their encoders struggle with memory constraints due to the large number of spots (often exceeding 10,000) in typical ST datasets. Herein, we propose STFlow, a flow matching generative model that considers cell-cell interaction by modeling the joint distribution of gene expression of an entire slide. It also employs an efficient slide-level encoder with local spatial attention, enabling whole-slide processing without excessive memory overhead. On the recently curated HEST-1k and STImage-1K4M benchmarks, STFlow substantially outperforms state-of-the-art baselines and achieves over 18% relative improvements over the pathology foundation models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。