Controllable Latent Space Augmentation for Digital Pathology
Whole-slide image (WSI) analysis in digital pathology faces dual challenges: extreme resolution (gigapixel-scale) and scarcity of dense annotations. Conventional patch-level augmentation incurs high computational overhead, while feature-level methods lack semantic controllability. To address this, we propose the first latent-space controllable augmentation framework tailored for WSIs. Our approach employs a conditional generative model explicitly grounded in histopathologically meaningful transformations—such as color tone adjustment and morphological erosion—to enable efficient, semantically consistent, whole-slide augmentation directly in the latent space. Integrated with multi-instance learning (MIL), it eliminates per-patch processing. Evaluated across multi-organ few-shot classification and segmentation tasks, our method significantly outperforms state-of-the-art augmentation strategies, achieving superior accuracy while maintaining high inference efficiency and deployment feasibility.