The advancement of Spatial Transcriptomics (ST) has facilitated the spatially-aware profiling of gene expressions based on histopathology images. Although ST data offers valuable insights into the micro-environment of tumors, its acquisition cost remains expensive. Therefore, directly predicting the ST expressions from digital pathology images is desired. Current methods usually adopt existing regression backbones along with patch-sampling for this task, which ignores the inherent multi-scale information embedded in the pyramidal data structure of digital pathology images, and wastes the inter-spot visual information crucial for accurate gene expression prediction. To address these limitations, we propose M2OST, a many-to-one regression Transformer that can accommodate the hierarchical structure of the pathology images via a decoupled multi-scale feature extractor. Unlike traditional models that are trained with one-to-one image-label pairs, M2OST uses multiple images from different levels of the digital pathology image to jointly predict the gene expressions in their common corresponding spot. Built upon our many-to-one scheme, M2OST can be easily scaled to fit different numbers of inputs, and its network structure inherently incorporates nearby inter-spot features, enhancing regression performance. We have tested M2OST on three public ST datasets and the experimental results show that M2OST can achieve state-of-the-art performance with fewer parameters and floating-point operations (FLOPs). The code is available at: https://github.com/Dootmaan/M2OST.

翻译：空间转录组学（ST）技术的进步促进了基于组织病理学图像的空间感知基因表达谱分析。尽管ST数据为肿瘤微环境提供了宝贵的见解，但其获取成本仍然昂贵。因此，直接从数字病理图像预测ST表达成为迫切需求。现有方法通常采用现成的回归主干网络配合图像块采样来完成此任务，这种方法忽略了数字病理图像金字塔数据结构中固有的多尺度信息，并浪费了对准确基因表达预测至关重要的斑点间视觉信息。为解决这些局限性，我们提出M2OST——一种多对一回归Transformer，它通过解耦的多尺度特征提取器适应病理图像的层次结构。与传统使用一对一图像-标签对进行训练的模型不同，M2OST利用来自数字病理图像不同层级的多个图像共同预测其共同对应斑点的基因表达。基于我们的多对一方案，M2OST可以轻松扩展以适应不同数量的输入，其网络结构天然融合了邻近斑点间的特征，从而提升了回归性能。我们在三个公开ST数据集上测试了M2OST，实验结果表明M2OST能够以更少的参数和浮点运算量（FLOPs）实现最先进的性能。代码已开源：https://github.com/Dootmaan/M2OST。