# The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

arXiv: https://arxiv.org/abs/2305.18033 · HTML: https://mlsanity.com/paper/2305.18033/

## 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 (most cited first, 12 of 12)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Automated Gleason Grading of Prostate Biopsies using Deep Learning](https://mlsanity.com/paper/1907.07980/) (2019)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer](https://mlsanity.com/paper/2009.08917/) (2020)
- [Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology](https://mlsanity.com/paper/2106.13150/) (2021)
- [ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology](https://mlsanity.com/paper/2211.13621/) (2022)
- [Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks](https://mlsanity.com/paper/2104.09310/) (2021)
- [A matrix-free approach to parallel and memory-efficient deformable image registration](https://mlsanity.com/paper/1804.10541/) (2018)
- [Using deep learning to detect patients at risk for prostate cancer despite benign biopsies](https://mlsanity.com/paper/2106.14256/) (2021)
- [Increasing the usefulness of already existing annotations through WSI registration](https://mlsanity.com/paper/2303.06727/) (2023)

## Built on by (most cited first, 11 of 11)

- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge](https://mlsanity.com/paper/2404.13108/) (2024)
- [DeeperHistReg: Robust Whole Slide Images Registration Framework](https://mlsanity.com/paper/2404.14434/) (2024)
- [SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion](https://mlsanity.com/paper/2410.01408/) (2024)
- [IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion](https://mlsanity.com/paper/2405.08197/) (2024)
- [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://mlsanity.com/paper/2502.19123/) (2025)
- [Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images](https://mlsanity.com/paper/2506.15853/) (2025)
- [A Unified Low-level Foundation Model for Enhancing Pathology Image Quality](https://mlsanity.com/paper/2509.01071/) (2025)
- [A Deployment-Friendly Foundational Framework for Efficient Computational Pathology](https://mlsanity.com/paper/2602.14010/) (2026)
- [PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learning](https://mlsanity.com/paper/2602.23292/) (2026)
- [UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift](https://mlsanity.com/paper/2503.20653/) (2025)

## 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.