A benchmarking platform for mitotic cell classification of ANA IIF HEp-2 images

Miros, Anastasia, Wiliem, Arnold, Holohan, Kim, Ball, Lauren, Hobson, Peter and Lovell, Brian C. (2015). A benchmarking platform for mitotic cell classification of ANA IIF HEp-2 images. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). International Conference on Digital Image Computing: Techniques and Applications, DICTA, Adelaide, SA, Australia, (9-14). 23-25 November 2015. doi:10.1109/DICTA.2015.7371213


Author Miros, Anastasia
Wiliem, Arnold
Holohan, Kim
Ball, Lauren
Hobson, Peter
Lovell, Brian C.
Title of paper A benchmarking platform for mitotic cell classification of ANA IIF HEp-2 images
Conference name International Conference on Digital Image Computing: Techniques and Applications, DICTA
Conference location Adelaide, SA, Australia
Conference dates 23-25 November 2015
Convener Jamie Sherrah
Proceedings title 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Journal name 2015 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2015
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/DICTA.2015.7371213
Open Access Status Not Open Access
ISBN 9781467367950
Start page 9
End page 14
Total pages 6
Collection year 2016
Language eng
Abstract/Summary Anti-nuclear antibody (ANA) indirect immunofluoresence (IIF) human epithelial type-2 (HEp-2) testing provides important clinical information for the diagnosis of systemic autoimmune rheumatic diseases (SARD). Recent developments in computer aided diagnosis (CAD) systems aim to improve the reliability and reproducibility of ANA IIF laboratory testing by providing ANA classification. The limited prior work into ANA IIF HEp-2 mitotic classification derives its protocol from clinically abstract performance metrics dependent on a single data set of FITC channel images. In this paper, we propose an extensive benchmarking platform considering both the DAPI and FITC channel images for mitotic classification. Furthermore, we argue that pedestrian detection metrics better define the mitotic classification problem. To support these propositions, we design a simple classifier on the DAPI channel and use meaningful performance metrics to demonstrate that it significantly outperforms the most recent state-of-the-art approach.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Fri, 11 Dec 2015, 12:59:57 EST by Arnold Wiliem on behalf of School of Information Technol and Elec Engineering