Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images
This addresses computational bottlenecks for researchers and practitioners in medical imaging using weakly supervised learning, though it is incremental as it adapts existing augmentation techniques to a new space.
The paper tackles the computational cost of data augmentation in whole-slide image analysis by proposing EmbAugmenter, a GAN that synthesizes augmentations in the embedding space instead of pixel space, resulting in performance on par with traditional methods while being substantially faster.
Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. To overcome this limitation, we present EmbAugmenter, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster.