发表机构
Cancer Research UK Scotland Institute; University of Glasgow（英国癌症研究苏格兰研究所; 格拉斯哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出轻量级Transformer框架MUL-T，以离散细胞标记的掩码上下文预测任务建模组织结构，在多项临床任务中性能优于经典基线，与基础ViT相当且成本更低。
AI 中文摘要
理解多重成像中的组织结构需要同时建模细胞表型及其空间上下文。现有方法通常依赖手工设计的特征，如标记强度统计或细胞类型比例，这些方法往往无法扩展或在具有异质性标记面板的队列中泛化。我们提出了MUL-T，一个轻量级Transformer框架，将组织结构重新定义为对离散细胞标记的掩码上下文预测任务。通过学习上下文化的[CLS]嵌入且无需特定任务监督，该模型能捕捉高阶细胞相互作用，同时保持计算效率。我们在多个临床相关下游任务上评估了MUL-T，包括核心级肿瘤模式分类、患者分级、PD-L1阳性预测及跨数据集治疗反应预测。在所有任务中，MUL-T均持续优于经典基于特征的基线，且尽管参数数量显著更少、训练成本更低，仍达到了与基础ViT模型相当的性能。
英文摘要
Understanding tissue organisation in multiplexed imaging requires modelling both cellular phenotypes and their spatial context. Existing approaches typically rely on handcrafted features, such as marker intensity statistics or cell-type proportions, which often fail to scale or generalise across cohorts with heterogeneous marker panels. We introduce MUL-T, a lightweight transformer framework that reframes tissue architecture as a masked contextual prediction task over discrete cell tokens. By learning contextualised [CLS] embeddings without task-specific supervision, the model captures higher-order cellular interactions while remaining computationally efficient. We evaluate MUL-T on several clinically relevant downstream tasks, including core-level tumour pattern classification, patient-level grading, PD-L1 positivity prediction, and cross-dataset treatment response prediction. Across tasks, MUL-T consistently outperforms classical feature-based baselines and achieves performance comparable to a foundation ViT model, despite substantially fewer parameters and lower training cost.
