arXiv 2604.210602026-05-20cs.CV

基于临床信息的儿童脑肿瘤全切片病理图像分类建模

Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images

Dept. of Computer Science（计算机科学系）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；School of Information（信息学院）；Dell Children's Medical Center（德尔儿童医学中心）；Dept. of OREI（OREI部门）；University of Nevada, Reno（内华达大学里诺分校）

Joakim Nguyen, Jian Yu, Jinrui Fang, Nicholas Konz, Tianlong Chen, Sanjay Krishnan, Chandra Krishnan, Ying Ding, Hairong Wang, Ankita Shukla

备注：: Accepted at the IEEE International Conference on Healthcare Informatics (ICHI), 2026

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AI总结：

本文提出一种结合临床信息的对比学习框架，用于在有限数据和类别不平衡条件下提高儿童脑肿瘤全切片图像的细粒度分类性能。

摘要：

准确诊断儿童脑肿瘤，从组织病理学开始，对深度学习提出了独特的挑战，包括严重的数据稀缺性、类别不平衡以及不同诊断亚型之间细微的形态学重叠。尽管病理基础模型在片段级表示学习方面取得了进展，但其在有限数据下有效适应弱监督的儿童脑肿瘤分类仍待探索。在本文中，我们引入了一种专家指导的对比微调框架，用于从全切片图像（WSI）中进行儿童脑肿瘤诊断。我们的方法将对比学习整合到滑动级别的多实例学习（MIL）中，以在下游微调过程中显式正则化滑动级别的表示几何。我们提出了一个通用的监督对比设置以及一个结合临床信息的硬负样本变体，旨在针对诊断上易混淆的亚型。通过在现实中的低样本和类别不平衡条件下对儿童脑肿瘤WSI分类进行全面实验，我们证明对比微调在细粒度诊断区分上产生了可测量的改进。我们的实验分析揭示了不同对比策略之间的互补优势，专家指导的硬负样本促进了更紧凑的类内表示和改进的类间分离。本文强调了在数据稀缺的儿童病理学设置中显式塑造滑动级别表示对于鲁棒细粒度分类的重要性。

英文摘要：

Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-grained morphologic overlap across diagnostically distinct subtypes. While pathology foundation models have advanced patch-level representation learning, their effective adaptation to weakly supervised pediatric brain tumor classification under limited data remains underexplored. In this work, we introduce an expert-guided contrastive fine-tuning framework for pediatric brain tumor diagnosis from whole-slide images (WSI). Our approach integrates contrastive learning into slide-level multiple instance learning (MIL) to explicitly regularize the geometry of slide-level representations during downstream fine-tuning. We propose both a general supervised contrastive setting and an expert-guided variant that incorporates clinically informed hard negatives targeting diagnostically confusable subtypes. Through comprehensive experiments on pediatric brain tumor WSI classification under realistic low-sample and class-imbalanced conditions, we demonstrate that contrastive fine-tuning yields measurable improvements in fine-grained diagnostic distinctions. Our experimental analyses reveal complementary strengths across different contrastive strategies, with expert-guided hard negatives promoting more compact intra-class representations and improved inter-class separation. This work highlights the importance of explicitly shaping slide-level representations for robust fine-grained classification in data-scarce pediatric pathology settings.

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