跨模态原型分配：计算病理学中基于病理切片-文本对比的无监督切片表示学习
Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology
AI总结：
提出ProAlign跨模态无监督框架，利用大语言模型生成原型文本并通过病理切片-文本对比学习，结合无参数注意力聚合，在四个公共数据集上超越现有无监督方法并媲美弱监督模型。
摘要：
随着病理学基础模型（FMs）的快速发展，全切片图像（WSIs）的表示学习日益受到关注。现有研究开发了高质量的病理切片特征提取器，并采用精心设计的聚合方案来获得切片级表示。然而，主流的弱监督切片表示学习方法主要基于多实例学习（MIL），针对特定下游任务定制，这限制了其泛化能力。为解决这一问题，一些研究探索了无监督切片表示学习。然而，这些方法仅关注切片的视觉模态，忽略了文本数据中蕴含的丰富语义信息。在本工作中，我们提出了ProAlign，一种跨模态的无监督切片表示学习框架。具体而言，我们利用大型语言模型（LLM）为WSI中存在的原型类型生成描述性文本，引入病理切片-文本对比来构建初始原型嵌入。此外，我们提出了一种无参数的注意力聚合策略，利用病理切片与这些原型之间的相似性来形成适用于广泛下游任务的无监督切片嵌入。在四个公共数据集上的大量实验表明，ProAlign优于现有的无监督框架，并达到了与某些弱监督模型相当的性能。
英文摘要：
With the rapid advancement of pathology foundation models (FMs), the representation learning of whole slide images (WSIs) attracts increasing attention. Existing studies develop high-quality patch feature extractors and employ carefully designed aggregation schemes to derive slide-level representations. However, mainstream weakly supervised slide representation learning methods, primarily based on multiple instance learning (MIL), are tailored to specific downstream tasks, which limits their generalizability. To address this issue, some studies explore unsupervised slide representation learning. However, these approaches focus solely on the visual modality of patches, neglecting the rich semantic information embedded in textual data. In this work, we propose ProAlign, a cross-modal unsupervised slide representation learning framework. Specifically, we leverage a large language model (LLM) to generate descriptive text for the prototype types present in a WSI, introducing patch-text contrast to construct initial prototype embeddings. Furthermore, we propose a parameter-free attention aggregation strategy that utilizes the similarity between patches and these prototypes to form unsupervised slide embeddings applicable to a wide range of downstream tasks. Extensive experiments on four public datasets show that ProAlign outperforms existing unsupervised frameworks and achieves performance comparable to some weakly supervised models.
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。
