mlsanityData to 14 Oct 2026

Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners

arXiv 2205.09048 ↗First posted 18 May 2022eess.IV cs.CVcited by 11

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Abstract (arXiv)

Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology. Compared to other medical images, pathology images are more difficult to annotate, and thus, there is an extreme lack of available datasets for conducting supervised learning to train robust deep learning models. In this paper, we propose a self-supervised learning (SSL) model, the global contrast-masked autoencoder (GCMAE), which can train the encoder to have the ability to represent local-global features of pathological images, also significantly improve the performance of transfer learning across data sets. In this study, the ability of the GCMAE to learn migratable representations was demonstrated through extensive experiments using a total of three different disease-specific hematoxylin and eosin (HE)-stained pathology datasets: Camelyon16, NCTCRC and BreakHis. In addition, this study designed an effective automated pathology diagnosis process based on the GCMAE for clinical applications. The source code of this paper is publicly available at https://github.com/StarUniversus/gcmae.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 18 May 2022.

Builds on

27 of 54 references.

References · 27

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15 more
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  3. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  4. 2020Self-supervised Learning: Generative or Contrastivecited by 694
  5. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 641
  6. 2021Masked Feature Prediction for Self-Supervised Visual Pre-Trainingcited by 576
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 3; 2025: 1; 2026: 1.

11 papers.

  1. 2022A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyondcited by 58
  2. 2023Masked Modeling for Self-supervised Representation Learning on Vision and Beyondcited by 13
  3. 2024A Survey of the Self Supervised Learning Mechanisms for Vision Transformerscited by 9
  4. 2023Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognitioncited by 8
  5. 2024Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentationcited by 6
  6. 2023EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiographycited by 4
  7. 2023Aerial Image Object Detection With Vision Transformer Detector (ViTDet)cited by 3
  8. 2023Efficient Masked Autoencoders with Self-Consistencycited by 2
  9. 2024Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learningcited by 1
  10. 2025Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learningcited by 0
  11. 2026AINet: Anchor Instances Learning for Regional Heterogeneity in Whole Slide Imagecited by 0