# HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization

arXiv: https://arxiv.org/abs/2111.04738 · HTML: https://mlsanity.com/paper/2111.04738/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 97 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis](https://mlsanity.com/paper/1901.04059/) (2019)
- [Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues](https://mlsanity.com/paper/1705.08369/) (2017)
- [Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images](https://mlsanity.com/paper/1905.10959/) (2019)
- [Weakly Supervised Clustering by Exploiting Unique Class Count](https://mlsanity.com/paper/1906.07647/) (2019)

## Built on by (most cited first, 8 of 8)

- [Phikon-v2, A large and public feature extractor for biomarker prediction](https://mlsanity.com/paper/2409.09173/) (2024)
- [WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering](https://mlsanity.com/paper/2407.05603/) (2024)
- [HEMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator](https://mlsanity.com/paper/2403.18501/) (2024)
- [The Importance of Downstream Networks in Digital Pathology Foundation Models](https://mlsanity.com/paper/2311.17804/) (2023)
- [Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?](https://mlsanity.com/paper/2502.20823/) (2025)
- [Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review](https://mlsanity.com/paper/2211.14847/) (2022)
- [Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images](https://mlsanity.com/paper/2312.06454/) (2023)
- [Robust sensitivity control in digital pathology via tile score distribution matching](https://mlsanity.com/paper/2502.20144/) (2025)

## Abstract (arXiv)

Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify breast cancer and define appropriate therapy, which depends on the evaluation of the expression of different biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including special techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. With the goal of reducing the number of steps and human bias in diagnosis, the HEROHE Challenge was organized, as a parallel event of the 16th European Congress on Digital Pathology, aiming to automate the assessment of the HER2 status based only on hematoxylin and eosin stained tissue sample of invasive breast cancer. Methods to assess HER2 status were presented by 21 teams worldwide and the results achieved by some of the proposed methods open potential perspectives to advance the state-of-the-art.