Discovering discriminative cell attributes for HEp-2 specimen image classification

Wiliem, Arnold, Hobson, Peter and Lovell, Brian C. (2014). Discovering discriminative cell attributes for HEp-2 specimen image classification. In: 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE Winter Conference on Applications of Computer Vision (WACV 2014), Steamboat Springs, CO, United States, (423-430). 24-26 March 2014. doi:10.1109/WACV.2014.6836071

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Author Wiliem, Arnold
Hobson, Peter
Lovell, Brian C.
Title of paper Discovering discriminative cell attributes for HEp-2 specimen image classification
Conference name IEEE Winter Conference on Applications of Computer Vision (WACV 2014)
Conference location Steamboat Springs, CO, United States
Conference dates 24-26 March 2014
Convener IEEE
Proceedings title 2014 IEEE Winter Conference on Applications of Computer Vision (WACV)   Check publisher's open access policy
Journal name 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/WACV.2014.6836071
ISBN 9781479949847
ISSN 1550-5790
Start page 423
End page 430
Total pages 8
Collection year 2015
Language eng
Abstract/Summary Recently, there has been a growing interest in developing Computer Aided Diagnostic (CAD) systems for improving the reliability and consistency of pathology test results. This paper describes a novel CAD system for the Anti-Nuclear Antibody (ANA) test via Indirect Immunofluorescence protocol on Human Epithelial Type 2 (HEp-2) cells. While prior works have primarily focused on classifying cell images extracted from ANA specimen images, this work takes a further step by focussing on the specimen image classification problem itself. Our system is able to efficiently classify specimen images as well as producing meaningful descriptions of ANA pattern class which helps physicians to understand the differences between various ANA patterns. We achieve this goal by designing a specimen-level image descriptor that: (1) is highly discriminative; (2) has small descriptor length and (3) is semantically meaningful at the cell level. In our work, a specimen image descriptor is represented by its overall cell attribute descriptors. As such, we propose two max-margin based learning schemes to discover cell attributes whilst still maintaining the discrimination of the specimen image descriptor. Our learning schemes differ from the existing discriminative attribute learning approaches as they primarily focus on discovering image-level attributes. Comparative evaluations were undertaken to contrast the proposed approach to various state-of-the-art approaches on a novel HEp-2 cell dataset which was specifically proposed for the specimen-level classification. Finally, we showcase the ability of the proposed approach to provide textual descriptions to explain ANA patterns.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 6836071

 
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Created: Mon, 19 May 2014, 20:46:22 EST by Arnold Wiliem on behalf of School of Information Technol and Elec Engineering