mlsanityData to 7 Oct 2026

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

arXiv 2210.17013 ↗First posted 31 Oct 2022cs.CV cs.AIcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

11 of 13 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  4. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  5. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  6. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  7. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  8. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  9. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 172
  10. 2020Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primarycited by 89
  11. 2020Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotationscited by 89

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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 2.

5 papers.

  1. 2023Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classificationcited by 18
  2. 2023AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Imagecited by 3
  3. 2025Controllable Latent Space Augmentation for Digital Pathologycited by 2
  4. 2024Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessmentscited by 0
  5. 2025Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classificationcited by 0