RankByGene: 通过跨模态排序一致性实现基因引导的组织病理学表示学习
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency
AI总结：
提出基于排序对齐损失的框架，利用教师-学生网络自监督知识蒸馏，解决空间转录组学与组织学图像的对齐问题，在基因表达预测、切片分类和生存分析任务中表现优异。
摘要：
空间转录组学通过映射组织内的基因表达提供必要的空间背景，从而能够详细研究细胞异质性和组织组织。然而，由于固有的空间扭曲和模态特异性变化，将ST数据与组织学图像对齐面临挑战。现有方法主要依赖直接对齐，通常无法捕捉复杂的跨模态关系。为解决这些限制，我们提出一种新颖框架，使用基于排序的对齐损失来对齐基因和图像特征，保留跨模态的相对相似性，并实现稳健的多尺度对齐。为进一步增强对齐的稳定性，我们采用教师-学生网络架构的自监督知识蒸馏，有效减轻基因表达数据中高维性、稀疏性和噪声带来的干扰。在涵盖基因表达预测、切片级分类和生存分析的七个公共数据集上的大量实验证明了我们方法的有效性，显示出比现有方法更好的对齐和预测性能。
英文摘要：
Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Existing methods largely rely on direct alignment, which often fails to capture complex cross-modal relationships. To address these limitations, we propose a novel framework that aligns gene and image features using a ranking-based alignment loss, preserving relative similarity across modalities and enabling robust multi-scale alignment. To further enhance the alignment's stability, we employ self-supervised knowledge distillation with a teacher-student network architecture, which serves as an intra-modal stability regularizer that prevents image-representation drift during cross-modal alignment. Extensive experiments on seven public datasets that encompass gene expression prediction, slide-level classification, and survival analysis demonstrate the efficacy of our method, showing improved alignment and predictive performance over existing methods. Code is available at https://github.com/winston52/RankByGene.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
