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

• URL: http://arxiv.org/abs/2508.09967v1
• Date: Wed, 13 Aug 2025 17:32:42 GMT
• Title: MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
• Authors: Tianqi Xiang, Yi Li, Qixiang Zhang, Xiaomeng Li,
• Abstract summary: Meta-language foundation models (VLFMs) have shown promise in addressing data scarcity for whole slide image (WSI) classification via zero-shot adaptation.<n>These methods remain outperformed by conventional multiple instance learning (MIL) approaches trained on large datasets.<n>While existing few-shot methods improve diagnostic accuracy with limited annotations, their reliance on conventional classifier designs introduces critical vulnerabilities to data scarcity.
• Score: 8.74505335529696
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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