MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
This work addresses data scarcity in clinical histopathology for improved diagnostic accuracy in few-shot settings, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of few-shot whole slide image classification by proposing a Meta-Optimized Classifier (MOC) that automatically optimizes classifier configurations, resulting in improved diagnostic accuracy with limited annotations, such as a 10.4% AUC gain over state-of-the-art methods on the TCGA-NSCLC benchmark.
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.