mlsanityData to 14 Oct 2026

Classifying Whole Slide Images: What Matters?

arXiv 2310.03279 ↗First posted 5 Oct 2023cs.CVcited by 0

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Abstract (arXiv)

Recently there have been many algorithms proposed for the classification of very high resolution whole slide images (WSIs). These new algorithms are mostly focused on finding novel ways to combine the information from small local patches extracted from the slide, with an emphasis on effectively aggregating more global information for the final predictor. In this paper we thoroughly explore different key design choices for WSI classification algorithms to investigate what matters most for achieving high accuracy. Surprisingly, we found that capturing global context information does not necessarily mean better performance. A model that captures the most global information consistently performs worse than a model that captures less global information. In addition, a very simple multi-instance learning method that captures no global information performs almost as well as models that capture a lot of global information. These results suggest that the most important features for effective WSI classification are captured at the local small patch level, where cell and tissue micro-environment detail is most pronounced. Another surprising finding was that unsupervised pre-training on a larger set of 33 cancers gives significantly worse performance compared to pre-training on a smaller dataset of 7 cancers (including the target cancer). We posit that pre-training on a smaller, more focused dataset allows the feature extractor to make better use of the limited feature space to better discriminate between subtle differences in the input patch.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 5 Oct 2023.

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15 of 26 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
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  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  4. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  5. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  6. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  7. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 359
  8. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  9. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  10. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  11. 2016Deep Learning for Identifying Metastatic Breast Cancercited by 93
  12. 2020Representation Learning of Histopathology Images using Graph Neural Networkscited by 40
3 more
  1. 2022Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Imagescited by 32
  2. 2022ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classificationcited by 21
  3. 2018Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathologycited by 16

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