FOCUS：面向少样本全玻片图像分类的知识增强自适应视觉压缩
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
AI总结：
针对少样本全玻片图像分类中图块冗余干扰诊断特征学习的问题，提出FOCUS框架，结合病理学基础模型与语言先验，通过三阶段渐进压缩聚焦关键区域，在多类癌症病理数据集上实现了更优的少样本诊断性能。
摘要：
少样本学习为计算病理学（CPath）中的癌症诊断提供了关键解决方案，可解决数据可用性方面的根本限制，尤其是专家标注稀缺和患者隐私约束问题。该范式的一项核心挑战源于有限的全玻片图像（WSIs）训练集与其包含的海量图块之间的固有差异——这些图块中有很大一部分缺乏诊断相关信息，可能会削弱模型学习并聚焦关键诊断特征的能力。尽管近期研究试图通过引入额外知识来解决这一问题，但仍存在几个关键缺口阻碍了进一步进展：（1）尽管功能强大的病理学基础模型（FMs）已经出现，但其潜力在很大程度上尚未得到挖掘，大多数方法仅将其用于基础特征提取；（2）当前的语言引导机制试图一次性将文本提示与大量WSI图块对齐，难以利用丰富的病理学语义信息。为此，我们提出了知识增强自适应视觉压缩框架，命名为FOCUS，该框架创新性地将病理学FMs与语言先验知识相结合，通过优先筛选具有判别性的WSI图块，实现对诊断相关区域的聚焦分析。我们的方法采用渐进式三阶段压缩策略：首先利用FMs进行全局视觉冗余消除，将压缩后的特征与语言提示融合以进行语义相关性评估，随后在保留空间连贯性的同时执行邻域感知视觉令牌过滤。在涵盖乳腺癌、肺癌和卵巢癌的病理学数据集上开展的大量实验表明，该方法在少样本病理学诊断中表现出优异性能。代码可在https://github.com/dddavid4real/FOCUS获取。
英文摘要：
Few-shot learning presents a critical solution for cancer diagnosis in computational pathology (CPath), addressing fundamental limitations in data availability, particularly the scarcity of expert annotations and patient privacy constraints. A key challenge in this paradigm stems from the inherent disparity between the limited training set of whole slide images (WSIs) and the enormous number of contained patches, where a significant portion of these patches lacks diagnostically relevant information, potentially diluting the model's ability to learn and focus on critical diagnostic features. While recent works attempt to address this by incorporating additional knowledge, several crucial gaps hinder further progress: (1) despite the emergence of powerful pathology foundation models (FMs), their potential remains largely untapped, with most approaches limiting their use to basic feature extraction; (2) current language guidance mechanisms attempt to align text prompts with vast numbers of WSI patches all at once, struggling to leverage rich pathological semantic information. To this end, we introduce the knowledge-enhanced adaptive visual compression framework, dubbed FOCUS, which uniquely combines pathology FMs with language prior knowledge to enable a focused analysis of diagnostically relevant regions by prioritizing discriminative WSI patches. Our approach implements a progressive three-stage compression strategy: we first leverage FMs for global visual redundancy elimination, and integrate compressed features with language prompts for semantic relevance assessment, then perform neighbor-aware visual token filtering while preserving spatial coherence. Extensive experiments on pathological datasets spanning breast, lung, and ovarian cancers demonstrate its superior performance in few-shot pathology diagnosis. Codes are available at https://github.com/dddavid4real/FOCUS.
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