TaxoMIL：用于层次化全切片图像分析的分层约束学习
TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
AI总结：
提出TaxoMIL框架，将WSI诊断重构为多粒度文本生成任务，通过双头Transformer解码器和分类学引导目标，实现层次感知的准确预测。
摘要：
全切片图像（WSI）分析是计算病理学的核心，多实例学习（MIL）已成为切片级诊断的标准流程。然而，传统方法将WSI诊断视为离散标签上的扁平分类任务，与临床推理固有的层次化、从粗到细的性质相矛盾。尽管最近的分层分类器和视觉语言模型（VLM）试图解决这一结构差距，但它们要么无法捕捉相关诊断之间的语义连续性，要么因不受约束的文本生成而产生分类学幻觉和父子标签违规。为解决这些局限性，我们提出TaxoMIL，一个分类学约束框架，将WSI诊断重构为多粒度文本生成任务。TaxoMIL利用双头Transformer解码器生成粗粒度和细粒度的诊断文本，并引入分类学引导目标，明确结构化标签嵌入空间，并严格将切片级视觉表示锚定在临床分类学中。在三个不同的WSI数据集上的大量实验表明，TaxoMIL始终优于最先进的MIL分类器和基于VLM的生成方法，产生准确且层次感知的诊断预测。代码发布在https://this URL。
英文摘要：
Whole slide image (WSI) analysis is central to computational pathology, with multiple instance learning (MIL) emerging as the standard pipeline for slide-level diagnosis. However, conventional approaches formulate WSI diagnosis as a flat classification task over discrete labels, contradicting the inherently hierarchical, coarse-to-fine nature of clinical reasoning. Although recent hierarchical classifiers and vision-language models (VLMs) have sought to address this structural gap, they either fail to capture semantic continuity between related diagnoses or suffer from unconstrained text generation that produces taxonomic hallucinations and parent-child label violations. To address these limitations, we propose TaxoMIL, a taxonomy-constrained framework that reformulates WSI diagnosis as a multi-granularity text generation task. TaxoMIL utilizes a dual-head Transformer decoder to generate coarse- and fine-level diagnostic text, and introduces taxonomy-guided objectives that explicitly structure the label embedding space and strictly ground slide-level visual representations within the clinical taxonomy. Extensive experiments across three diverse WSI datasets demonstrate that TaxoMIL consistently outperforms state-of-the-art MIL classifiers and VLM-based generative methods, yielding accurate and hierarchy-aware diagnostic predictions. The code is released at https://github.com/QuIIL/TaxoMIL
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。
