Learnable frozen feature augmentation for few-shot biomarker prediction from pathology whole-slide images
Di Zhang, Jiashuai Liu, Youyuan Ma, Jiusong Ge, Zhi Zeng, Wenfang Sun, Qidong Liu, Kai He, Yefeng Zheng, Weimiao Yu, Chen Li, Zeyu GaoAbstract
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.