mlsanityData to 14 Oct 2026

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

arXiv 1705.08369 ↗First posted 23 May 2017cs.CV cs.AI q-bio.QMcited by 16

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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.

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

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Citing papers per year: 2021: 2; 2022: 4; 2023: 5; 2024: 2; 2025: 3.

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  1. 2021SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancercited by 31
  2. 2024Multistain Pretraining for Slide Representation Learning in Pathologycited by 28
  3. 2021HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridizationcited by 8
  4. 2023Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Imagescited by 5
  5. 2023LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Datasetcited by 3
  6. 2024Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Samplingcited by 3
  7. 2022Interpretable HER2 scoring by evaluating clinical Guidelines through a weakly supervised, constrained Deep Learning Approachcited by 2
  8. 2022convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancercited by 2
  9. 2025From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Reviewcited by 2
  10. 2025Adaptive Diagnostic Reasoning Framework for Pathology with Multimodal Large Language Modelscited by 2
  11. 2023A comprehensive review of deep learning in lung cancercited by 1
  12. 2023Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry imagescited by 1
4 more
  1. 2023Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Imagescited by 1
  2. 2022A Semantic Segmentation Network Based Real-Time Computer-Aided Diagnosis System for Hydatidiform Mole Hydrops Lesion Recognition in Microscopic Viewcited by 0
  3. 2022Gold-standard of HER2 breast cancer biopsies using supervised learning based on multiple pathologist annotationscited by 0
  4. 2025Scalable Whole Slide Image Representation Using K-Mean Clustering and Fisher Vector Aggregationcited by 0