Learnable frozen feature augmentation for few-shot biomarker prediction from pathology whole-slide images.

MOTIVATION: Whole-slide image (WSI)-based biomarker prediction in computational pathology has the potential to support scalable and resource-efficient analysis of large pathology cohorts, helping prioritize cases for downstream molecular testing and patient stratification. However, reliable biomarker labels are often limited and costly to obtain, making label-efficient WSI prediction essential. Recent slide-level foundation models have opened new opportunities for few-shot biomarker prediction by providing strong pretrained slide representations. Nevertheless, few-shot learning still suffers from sparse labeled support data, and data augmentation remains important for improving robustness and generalization. In frozen multi-stage WSI pipelines, however, conventional augmentation is difficult to apply: pixel-level augmentation requires costly feature re-extraction, while naive perturbation of frozen representations may compromise semantic consistency. RESULTS: To address this challenge, we propose Learnable Frozen Feature Augmentation (LFFA), a training-time feature-space augmentation framework for few-shot WSI biomarker prediction. Instead of directly perturbing frozen slide features, LFFA learns controllable augmented slide views from contextualized interaction tokens under geometry-aware and downstream-supervised constraints, improving representation diversity while preserving semantic consistency. The augmented features are further optimized with an augmented-class α-mix loss to balance diversity and class semantics. We evaluate LFFA on three few-shot WSI biomarker prediction tasks, covering molecular marker prediction and gene mutation prediction, using three representative and widely used slide-level foundation models. Results show that LFFA consistently improves the corresponding baselines and achieves stronger overall robustness than existing augmentation methods across tasks, backbones, and shot settings. AVAILABILITY AND IMPLEMENTATION: Source code and processed experimental splits will be made available at https://github.com/zdipath/LFFA.