发表机构
National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出MSGR模型，利用GO的基因功能层级结构作为先验，改进组织病理图像的空间基因表达预测，在9个HEST-1k数据集上验证其性能优于平坦解码方法。
AI 中文摘要
从组织病理图像预测空间基因表达可实现大规模转录组分析，无需直接测量的高昂成本。现有方法将目标基因集解码为平坦、无结构的向量，忽略了基因间因共享生物通路和调控程序产生的依赖关系。缺乏明确的结构引导，模型必须完全从有限的配对数据中推断这些依赖关系，限制了预测质量。我们提出MSGR（多尺度基因优化器），通过引入基因本体论（GO，经整理的基因功能层级结构）作为明确的结构先验，填补这一空白。MSGR将目标基因组织为四级GO树，其GO引导解码器在尺度加权监督下，通过残差校正逐步将预测从粗粒度功能域细化到细粒度单个基因。仅在基因侧运行的GO引导解码器可作为无缝插件替换，无需任何图像侧修改即可持续改进现有架构。在HEST-1k基准的9个数据集上进行的大量实验为两个核心主张提供了实证证据：GO结构化解码始终优于平坦解码，甚至优于最先进的生成基线；且这种增益可归因于生物本体结构，而非单纯的层级分解，这一点通过与结构等效的随机层级相比获得+0.027的优势得到证实。
英文摘要
Predicting spatial gene expression from histopathology images enables large-scale transcriptomic profiling without the cost of direct measurement. Existing methods decode the target gene set as a flat, unstructured vector, ignoring the inter-gene dependencies arising from shared biological pathways and regulatory programs. Without explicit structural guidance, models must infer these dependencies entirely from limited paired data, constraining prediction quality. We propose MSGR (Multi-Scale Gene Refiner), which bridges this gap by incorporating the Gene Ontology (GO), a curated functional hierarchy of genes, as an explicit structural prior. MSGR organizes target genes into a four-level GO tree. Its GO-guided decoder then progressively refines predictions from coarse functional domains to fine individual genes via residual corrections under scale-weighted supervision. Operating solely on the gene side, the GO-guided decoder serves as a seamless plug-in replacement that consistently improves existing architectures without requiring any image-side modifications. Extensive experiments on nine datasets from the HEST-1k benchmark provide empirical evidence for two central claims: GO-structured decoding consistently outperforms flat decoding, even against a state-of-the-art generative baseline, and the gain is attributable to biological ontology structure rather than hierarchical decomposition per se, as confirmed by a +0.027 margin over a structurally equivalent random hierarchy.
CommentsAccepted by ACM MM 2026. Code: https://github.com/NozomiMizore/MSGR
