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

arXiv: https://arxiv.org/abs/2210.17013 · HTML: https://mlsanity.com/paper/2210.17013/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 13 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary](https://mlsanity.com/paper/2006.13932/) (2020)
- [Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations](https://mlsanity.com/paper/2008.05571/) (2020)

## Built on by (most cited first, 5 of 5)

- [Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification](https://mlsanity.com/paper/2306.16180/) (2023)
- [AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image](https://mlsanity.com/paper/2303.06371/) (2023)
- [Controllable Latent Space Augmentation for Digital Pathology](https://mlsanity.com/paper/2508.14588/) (2025)
- [Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments](https://mlsanity.com/paper/2406.07061/) (2024)
- [Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification](https://mlsanity.com/paper/2508.03081/) (2025)

## Abstract (arXiv)

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.