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

2026-04-22

AI Summary

This work addresses the challenge of fine-grained classification in pediatric brain tumor whole-slide images, where data scarcity, class imbalance, and high morphological similarity among subtypes hinder accurate diagnosis. To tackle these issues, the authors propose an expert-guided contrastive fine-tuning framework that integrates contrastive learning into multiple instance learning. During fine-tuning, the method explicitly regularizes the geometric structure of whole-slide-level representations and introduces a novel clinical knowledge-driven hard negative mining strategy. This expert-informed contrastive approach specifically targets easily confused subtypes, enhancing intra-class compactness and inter-class separability. Evaluated under realistic low-data and imbalanced conditions, the framework significantly improves the fine-grained discriminative capability of weakly supervised diagnosis, demonstrating the effectiveness and complementary advantages of the proposed contrastive strategy.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• pediatric brain tumor
• whole-slide histopathology
• data scarcity
• class imbalance
• fine-grained classification

Innovation

• contrastive fine-tuning
• expert-guided hard negatives
• whole-slide image classification
• multiple instance learning
• pediatric brain tumor