A Robust Algorithm for Automated HER2 Scoring in Breast Cancer Histology Slides Using Characteristic Curves
Type of content
UC permalink
Publisher's DOI/URI
Thesis discipline
Degree name
Publisher
Journal Title
Journal ISSN
Volume Title
Language
Date
Authors
Abstract
This paper presents a novel feature descriptor and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI). Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level. We propose an efficient method based on characteristic curves which encode all relevant information in a smooth polynomial curve with the percentage of stained membranes plotted against variations in intensity/saturation of the colour thresholds used for segmentation. Our algorithm performed exceedingly well at a recent online contest held by the University of Warwick [1], obtaining the second best points score of 390 out of 420 and the overall seventh position in the combined leaderboard [2]. The paper describes three classification algorithms with features extracted from characteristic curves and provides experimental results and comparative analysis
Description
Citation
Keywords
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Field of Research::11 - Medical and Health Sciences::1112 - Oncology and Carcinogenesis::111202 - Cancer Diagnosis
Fields of Research::32 - Biomedical and clinical sciences::3202 - Clinical sciences::320202 - Clinical chemistry (incl. diagnostics)
Fields of Research::32 - Biomedical and clinical sciences::3202 - Clinical sciences::320220 - Pathology (excl. oral pathology)