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Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
- Publication Year :
- 2017
-
Abstract
- 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. \ud \ud Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues. Available from: https://www.researchgate.net/publication/317087832_Her2_Challenge_Contest_A_Detailed_Assessment_of_Automated_Her2_Scoring_Algorithms_in_Whole_Slide_Images_of_Breast_Cancer_Tissues [accessed Jul 31, 2017].
- Subjects :
- FOS: Computer and information sciences
0301 basic medicine
Histology
Receptor, ErbB-2
Computer science
Computer Science - Artificial Intelligence
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Breast Neoplasms
Haematoxylin
CONTEST
Quantitative Biology - Quantitative Methods
Pathology and Forensic Medicine
RC0254
03 medical and health sciences
chemistry.chemical_compound
0302 clinical medicine
Breast cancer
Invasive breast carcinoma
Image Interpretation, Computer-Assisted
Biomarkers, Tumor
medicine
Humans
skin and connective tissue diseases
Quantitative Methods (q-bio.QM)
Eosin
Visual examination
General Medicine
medicine.disease
Immunohistochemistry
Clinical Practice
Artificial Intelligence (cs.AI)
030104 developmental biology
chemistry
FOS: Biological sciences
030220 oncology & carcinogenesis
Female
Algorithm
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 03090167
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....11f722b3984ae5f3205956e29aab23d5