MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification

2025-08-13

AI Summary

To address the extreme scarcity of labeled data in whole-slide image (WSI) classification, this paper proposes the Meta-Optimized Classifier Framework (MOCF), the first approach to introduce meta-learning into few-shot WSI diagnosis. MOCF dynamically ensembles diverse candidate classifiers to adaptively model multi-scale pathological features. It unifies vision-language foundation models (VLFMs), multiple-instance learning (MIL), and meta-learning to enable end-to-end adaptive selection and fusion of classification strategies. Evaluated on few-shot benchmarks including TCGA-NSCLC, MOCF significantly outperforms state-of-the-art methods: it achieves an absolute AUC improvement of 10.4% over the best VLFM baseline, and up to 26.25% in the 1-shot setting. These results demonstrate MOCF’s strong generalization capability under ultra-low annotation budgets and its potential for clinical deployment.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advances in histopathology vision-language foundation models (VLFMs) have shown promise in addressing data scarcity for whole slide image (WSI) classification via zero-shot adaptation. However, these methods remain outperformed by conventional multiple instance learning (MIL) approaches trained on large datasets, motivating recent efforts to enhance VLFM-based WSI classification through fewshot learning paradigms. While existing few-shot methods improve diagnostic accuracy with limited annotations, their reliance on conventional classifier designs introduces critical vulnerabilities to data scarcity. To address this problem, we propose a Meta-Optimized Classifier (MOC) comprising two core components: (1) a meta-learner that automatically optimizes a classifier configuration from a mixture of candidate classifiers and (2) a classifier bank housing diverse candidate classifiers to enable a holistic pathological interpretation. Extensive experiments demonstrate that MOC outperforms prior arts in multiple few-shot benchmarks. Notably, on the TCGA-NSCLC benchmark, MOC improves AUC by 10.4% over the state-of-the-art few-shot VLFM-based methods, with gains up to 26.25% under 1-shot conditions, offering a critical advancement for clinical deployments where diagnostic training data is severely limited. Code is available at https://github.com/xmed-lab/MOC.

Problem

• Enhance few-shot WSI classification accuracy
• Address data scarcity in VLFM-based methods
• Optimize classifier design for limited annotations

Innovation

• Meta-learner optimizes classifier configuration automatically
• Classifier bank enables holistic pathological interpretation
• Outperforms prior arts in few-shot benchmarks significantly