面向少样本全切片图像分类的病理知识增强多实例提示学习
Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification
AI总结：
针对少样本弱监督全切片图像分类数据与标签稀缺的问题，提出病理知识增强的多实例提示学习框架，整合多模态先验并建模层级关系，在三项临床任务上性能优于现有少样本方法。
摘要：
当前用于病理图像分析的多实例学习算法通常需要大量全切片图像（WSI）才能实现有效训练，但在学习数据有限的场景下性能欠佳。在临床环境中，由于患者隐私问题以及罕见病或新发疾病的普遍存在，病理切片的获取受限是不可避免的。少样本弱监督WSI分类任务的出现，应对了诊断中切片数据有限且切片级标签稀疏的重大挑战。基于预训练模型（例如CLIP）的提示学习似乎是适用于该场景的一种有前景的方案；然而，该领域的当前研究较为有限，现有算法往往仅关注补丁级提示或局限于语言提示。本文提出了一种经病理知识增强的多实例提示学习框架，即在补丁和切片两个层级的提示中整合视觉与文本先验知识。训练过程结合使用静态提示与可学习提示，有效引导预训练模型的激活，进一步助力关键病理模式的诊断。本文引入了轻量级的Messenger（自注意力）层与Summary（注意力池化）层，用于对同一患者数据内补丁与切片之间的关系建模。此外，对齐对比损失确保了补丁与切片的视觉可学习提示和文本可学习提示在特征层面的对齐。我们的方法在三项具有挑战性的临床任务中展现出优异性能，显著优于对比的少样本方法。
英文摘要：
Current multi-instance learning algorithms for pathology image analysis often require a substantial number of Whole Slide Images for effective training but exhibit suboptimal performance in scenarios with limited learning data. In clinical settings, restricted access to pathology slides is inevitable due to patient privacy concerns and the prevalence of rare or emerging diseases. The emergence of the Few-shot Weakly Supervised WSI Classification accommodates the significant challenge of the limited slide data and sparse slide-level labels for diagnosis. Prompt learning based on the pre-trained models (\eg, CLIP) appears to be a promising scheme for this setting; however, current research in this area is limited, and existing algorithms often focus solely on patch-level prompts or confine themselves to language prompts. This paper proposes a multi-instance prompt learning framework enhanced with pathology knowledge, \ie, integrating visual and textual prior knowledge into prompts at both patch and slide levels. The training process employs a combination of static and learnable prompts, effectively guiding the activation of pre-trained models and further facilitating the diagnosis of key pathology patterns. Lightweight Messenger (self-attention) and Summary (attention-pooling) layers are introduced to model relationships between patches and slides within the same patient data. Additionally, alignment-wise contrastive losses ensure the feature-level alignment between visual and textual learnable prompts for both patches and slides. Our method demonstrates superior performance in three challenging clinical tasks, significantly outperforming comparative few-shot methods.
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。
