用注意力机制融合多通道病理图像，提升肺癌预后预测能力
Fluoroformer: Scaling multiple instance learning to multiplexed images via attention-based channel fusion
- 通过注意力机制融合多通道图像特征，实现可解释的跨通道信息整合
- 在434例肺癌样本上达到强预后预测性能，复现免疫肿瘤标志物特征
- 专为新型多重染色病理技术设计，适合空间生物学与病理AI研究者
尽管多实例学习（MIL）是计算病理学处理全切片图像（WSIs）的基础方法，但现有方法主要针对传统苏木精-伊红（H&E）染色切片，难以适配新兴的多重染色技术。本文提出一种专为多重染色全切片图像设计的MIL策略——Fluoroformer模块，利用缩放点积注意力（SDPA）可解释地融合不同通道的信息。在包含434例非小细胞肺癌（NSCLC）样本的队列中，Fluoroformer不仅实现了优异的预后预测性能，还能复现NSCLC的免疫肿瘤学特征。该方法为将先进AI技术适配新兴空间生物学检测提供了可行路径。
原文摘要 · Abstract (English)
Though multiple instance learning (MIL) has been a foundational strategy in computational pathology for processing whole slide images (WSIs), current approaches are designed for traditional hematoxylin and eosin (H&E) slides rather than emerging multiplexed technologies. Here, we present an MIL strategy, the Fluoroformer module, that is specifically tailored to multiplexed WSIs by leveraging scaled dot-product attention (SDPA) to interpretably fuse information across disparate channels. On a cohort of 434 non-small cell lung cancer (NSCLC) samples, we show that the Fluoroformer both obtains strong prognostic performance and recapitulates immuno-oncological hallmarks of NSCLC. Our technique thereby provides a path for adapting state-of-the-art AI techniques to emerging spatial biology assays.
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