# Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues

arXiv: https://arxiv.org/abs/1705.08369 · HTML: https://mlsanity.com/paper/1705.08369/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 16 of 16)

- [SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer](https://mlsanity.com/paper/2110.06042/) (2021)
- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization](https://mlsanity.com/paper/2111.04738/) (2021)
- [Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images](https://mlsanity.com/paper/2302.09682/) (2023)
- [LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset](https://mlsanity.com/paper/2301.06304/) (2023)
- [Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling](https://mlsanity.com/paper/2404.00837/) (2024)
- [Interpretable HER2 scoring by evaluating clinical Guidelines through a weakly supervised, constrained Deep Learning Approach](https://mlsanity.com/paper/2211.09559/) (2022)
- [convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer](https://mlsanity.com/paper/2211.10690/) (2022)
- [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://mlsanity.com/paper/2502.19123/) (2025)
- [Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Models](https://mlsanity.com/paper/2511.12008/) (2025)
- [A comprehensive review of deep learning in lung cancer](https://mlsanity.com/paper/2308.02528/) (2023)
- [Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images](https://mlsanity.com/paper/2311.13719/) (2023)
- [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)
- [A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic View](https://mlsanity.com/paper/2204.04949/) (2022)
- [Gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotations](https://mlsanity.com/paper/2211.04649/) (2022)
- [Scalable Whole Slide Image Representation Using K-Mean Clustering and Fisher Vector Aggregation](https://mlsanity.com/paper/2501.12085/) (2025)

## Abstract (arXiv)

Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.