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

TL;DR

This paper introduces MOC, a meta-optimized classifier that significantly improves few-shot whole slide image classification by automatically selecting and optimizing classifiers, outperforming existing methods especially in data-scarce scenarios.

Contribution

The paper presents a novel meta-learner and classifier bank framework that enhances few-shot WSI classification, addressing vulnerabilities of conventional classifiers under limited data conditions.

Findings

MOC outperforms prior methods on multiple benchmarks.

Achieves 10.4% higher AUC on TCGA-NSCLC benchmark.

Up to 26.25% improvement in 1-shot scenarios.

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.