# Predicting Ki67, ER, PR, and HER2 Statuses from H&E-stained Breast Cancer Images

arXiv: https://arxiv.org/abs/2308.01982 · HTML: https://mlsanity.com/paper/2308.01982/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Representer Point Selection for Explaining Deep Neural Networks](https://mlsanity.com/paper/1811.09720/) (2018)
- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [Do Explanations Explain? Model Knows Best](https://mlsanity.com/paper/2203.02269/) (2022)
- [Extending Unsupervised Neural Image Compression With Supervised Multitask Learning](https://mlsanity.com/paper/2004.07041/) (2020)
- [GPEX, A Framework For Interpreting Artificial Neural Networks](https://mlsanity.com/paper/2112.09820/) (2021)

## Built on by (most cited first, 14 of 14)

- [MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine](https://mlsanity.com/paper/2408.02900/) (2024)
- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [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)
- [WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression](https://mlsanity.com/paper/2503.18074/) (2025)
- [USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining](https://mlsanity.com/paper/2507.05843/) (2025)
- [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 Pixels to Pathology: Restoration Diffusion for Diagnostic-Consistent Virtual IHC](https://mlsanity.com/paper/2508.02528/) (2025)
- [PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learning](https://mlsanity.com/paper/2602.23292/) (2026)
- [A Generative Foundation Model for Multimodal Histopathology](https://mlsanity.com/paper/2604.03635/) (2026)
- [Computational Methods for Breast Cancer Molecular Profiling through Routine Histopathology: A Review](https://mlsanity.com/paper/2412.10392/) (2024)
- [SCFANet: Style Distribution Constraint Feature Alignment Network For Pathological Staining Translation](https://mlsanity.com/paper/2504.00490/) (2025)
- [Feature Learning with Multi-Stage Vision Transformers on Inter-Modality HER2 Status Scoring and Tumor Classification on Whole Slides](https://mlsanity.com/paper/2512.22335/) (2025)
- [PAINT: Pathology-Aware Integrated Next-Scale Transformation for Virtual Immunohistochemistry](https://mlsanity.com/paper/2601.16024/) (2026)

## Abstract (arXiv)

Despite the advances in machine learning and digital pathology, it is not yet clear if machine learning methods can accurately predict molecular information merely from histomorphology. In a quest to answer this question, we built a large-scale dataset (185538 images) with reliable measurements for Ki67, ER, PR, and HER2 statuses. The dataset is composed of mirrored images of H&E and corresponding images of immunohistochemistry (IHC) assays (Ki67, ER, PR, and HER2. These images are mirrored through registration. To increase reliability, individual pairs were inspected and discarded if artifacts were present (tissue folding, bubbles, etc). Measurements for Ki67, ER and PR were determined by calculating H-Score from image analysis. HER2 measurement is based on binary classification: 0 and 1+ (IHC scores representing a negative subset) vs 3+ (IHC score positive subset). Cases with IHC equivocal score (2+) were excluded. We show that a standard ViT-based pipeline can achieve prediction performances around 90% in terms of Area Under the Curve (AUC) when trained with a proper labeling protocol. Finally, we shed light on the ability of the trained classifiers to localize relevant regions, which encourages future work to improve the localizations. Our proposed dataset is publicly available: https://ihc4bc.github.io/