蛋白质衍生知识能否改进病理学基础模型？
Can Protein-Derived Knowledge Improve Pathology Foundation Models?
浏览论文内容
中文总结 AI 辅助
提出三阶段框架ProSlide，利用蛋白质组知识改进病理基础模型，通过解耦蛋白质获取与跨模态迁移，在12个下游任务中取得最优性能。
中文摘要 AI 辅助
分子引导的病理学基础模型（PFMs）利用转录组或蛋白质组信息来丰富全切片图像（WSI）的表征，然而有效利用大型独立分子语料库仍然具有挑战性。首先，现有的分子基础模型编码蛋白质序列或单细胞状态，而非与WSI配对的病人层面的批量表达谱。其次，由于跨模态监督仅限于配对的WSI-组学样本，独立分子语料库的知识仅间接到达病理学编码器，形成了配对支持瓶颈。为解决这些挑战，我们提出了一个三阶段框架，将蛋白质组知识获取与跨模态迁移解耦，从而得到ProSlide，一个切片级别的层次化病理学基础模型。首先，为弥合模态差距，我们使用虚拟轮廓生成和表达空间多视图预训练，在12,695个样本级别的批量蛋白质谱上预训练了一个蛋白质组基础编码器（PFE）。其次，我们预训练ProSlide，一个补丁-区域-切片编码器，以从配对的WSI-蛋白质样本中预测蛋白质表达。第三，为缓解配对支持瓶颈，我们引入了Prot2Path，一种跨模态关系蒸馏目标。对于每个配对样本，它在一个共享的、冻结的PFE编码的配对和独立轮廓库上，对齐WSI及其蛋白质谱的相似性分布。我们在乳腺、肺和肾癌的12个下游任务上评估了ProSlide。尽管仅使用2,229个WSI和12,695个样本级别的蛋白质谱进行预训练，ProSlide在每个癌症组内均达到了最高的平均准确率和AUC。
英文摘要
Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations, yet effectively leveraging large standalone molecular corpora remains challenging. First, existing molecular foundation models encode protein sequences or single-cell states, not the patient-level bulk expression profiles paired with WSIs. Second, because cross-modal supervision is restricted to paired WSI-omics samples, knowledge from standalone molecular corpora reaches the pathology encoder only indirectly, creating a paired-support bottleneck. To address these challenges, we propose a three-stage framework that decouples proteomic knowledge acquisition from cross-modal transfer, yielding ProSlide, a slide-level hierarchical pathology foundation model. First, to close the modality gap, we pretrain a Proteomic Foundation Encoder (PFE) on 12,695 sample-level bulk protein profiles using virtual profile generation and expression-space multi-view pretraining. Second, we pretrain ProSlide, a patch-region-slide encoder, to predict protein expression from paired WSI-protein samples. Third, to relax the paired-support bottleneck, we introduce Prot2Path, a cross-modal relational distillation objective. For each paired sample, it aligns the similarity distributions of the WSI and its protein profile over a shared, frozen bank of PFE-encoded paired and standalone profiles. We evaluate ProSlide on 12 downstream tasks across breast, lung, and renal cancers. Despite being pretrained with only 2,229 WSIs and 12,695 sample-level protein profiles, ProSlide achieves the highest mean accuracy and AUC within each cancer group.
