Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

TL;DR

This paper introduces EmbAugmenter, a novel embedding space data augmentation method using a DA-GAN, which enhances weakly supervised learning on whole-slide images by reducing computational costs and improving performance.

Contribution

The paper proposes EmbAugmenter, a GAN-based augmentation technique operating in embedding space, offering faster training and comparable or better results than traditional patch-level augmentation.

Findings

Outperforms MIL without augmentation

Matches traditional patch-level augmentation performance

Significantly reduces computational requirements

Abstract

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.