arXiv 2607.004992026-07-03cs.CV

先验锚定去偏方法用于长尾多器官病理报告生成

Prior-Anchored Debiasing for Long-Tailed Multi-Organ Pathology Report Generation

City University of Hong Kong, Hong Kong SAR（香港城市大学）；University of Nottingham, United Kingdom（诺丁汉大学）；University of Massachusetts Boston, United States（马萨诸塞大学波士顿分校）

Feng Yang, Jie Liu, Yubo Pang, Peilin Chen, Xinheng Lyu, Shiqi Wang, Howard Leung, Ping Chen

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AI总结：

针对多器官病理报告生成中视觉表示和文本解码的长尾偏差，提出先验锚定框架PriOrGen，通过视觉原型锚定瓶颈和元报告锚定库分别缓解两类偏差，在长尾数据集上优于现有方法。

摘要：

从全切片图像自动生成病理报告在数字病理学中日益受到关注。然而，现有方法主要针对单器官场景开发，忽略了临床实践中遇到的多器官场景，其中器官类型通常遵循长尾分布。为解决这一问题，我们识别出两种关键偏差：（1）视觉表示偏差，编码器倾向于头部类模式而非尾部类判别特征；（2）文本解码偏差，解码器过拟合头部类叙事模式，导致尾部类器官的诊断输出不可靠。为缓解这两种偏差，我们提出一种新颖的先验锚定多器官病理报告生成框架（PriOrGen）。具体地，视觉原型锚定瓶颈模块利用信息瓶颈原理和可学习锚定表示，选择性地保留诊断相关视觉信息，同时过滤头部偏差冗余。其次，元报告锚定库模块构建器官特定的元报告锚定库，并检索器官忠实文本先验，引导解码器远离头部类叙事模式。在多器官病理数据集上的大量实验表明，我们的方法有效缓解了长尾偏差，并在头部和尾部器官类别上均实现了优于现有方法的报告生成性能。

英文摘要：

Automated pathology report generation from Whole Slide Images (WSIs) has attracted increasing attention in digital pathology. However, existing methods are predominantly developed under single-organ settings, overlooking the multi-organ scenarios encountered in clinical practice, where organ types typically follow a long-tailed distribution. To address this gap, we identify two critical biases: (1) visual representation bias, where the encoder favors head-class patterns over tail-class discriminative features, and (2) textual decoding bias, where the decoder overfits to head-class narrative patterns, yielding diagnostically unreliable outputs for tail-class organs. To mitigate these two biases, we propose a novel Prior-anchored multi-Organ pathology report Generation framework (PriOrGen). Specifically, a Visual-Prototype Anchored Bottleneck module leverages the information bottleneck principle with learnable anchor representations to selectively retain diagnostically relevant visual information while filtering out head-biased redundancy. Secondly, a Meta-Report Anchored Bank module constructs an organ-specific meta-report anchored bank and retrieves organ-faithful textual priors to steer the decoder away from head-class narrative patterns. Extensive experiments on a multi-organ pathology dataset demonstrate that our method effectively mitigates long-tail biases and achieves superior report generation performance across both head and tail organ categories compared to state-of-the-art 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。