mlsanityData to 14 Oct 2026

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv 2305.18033 ↗First posted 29 May 2023eess.IV cs.CVcited by 11

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Abstract (arXiv)

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 12 of its 63 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

12 of 63 references.

References · 12

  1. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,443
  2. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,320
  3. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
  4. 2019Automated Gleason Grading of Prostate Biopsies using Deep Learningcited by 69
  5. 2020Deep learning-based transformation of the H&E stain into special stainscited by 55
  6. 2020Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancercited by 21
  7. 2021Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathologycited by 12
  8. 2022ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathologycited by 10
  9. 2021Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networkscited by 9
  10. 2018A matrix-free approach to parallel and memory-efficient deformable image registrationcited by 4
  11. 2021Using deep learning to detect patients at risk for prostate cancer despite benign biopsiescited by 2
  12. 2023Increasing the usefulness of already existing annotations through WSI registrationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 2.

11 papers.

  1. 2024Multistain Pretraining for Slide Representation Learning in Pathologycited by 28
  2. 2024RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challengecited by 7
  3. 2024DeeperHistReg: Robust Whole Slide Images Registration Frameworkcited by 6
  4. 2024SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusioncited by 3
  5. 2024IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusioncited by 2
  6. 2025From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Reviewcited by 2
  7. 2025Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Imagescited by 1
  8. 2025A Unified Low-level Foundation Model for Enhancing Pathology Image Qualitycited by 1
  9. 2026A Deployment-Friendly Foundational Framework for Efficient Computational Pathologycited by 1
  10. 2026PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learningcited by 1
  11. 2025UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shiftcited by 0