Authors:
Telmo Amaral
1
;
Stephen McKenna
1
;
Katherine Robertson
2
and
Alastair Thompson
2
Affiliations:
1
School of Computing, University of Dundee, United Kingdom
;
2
School of Medicine, University of Dundee, United Kingdom
Keyword(s):
Breast tissue microarrays, Scoring, Immunohistochemistry, Ordinal regression.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Statistical Approach
;
Theory and Methods
Abstract:
Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours in a single histological section, but their scoring by pathologists is time consuming, typically highly quantised, and not without error. This paper compares the results of different classification and ordinal regression algorithms trained to predict the scores of immunostained breast TMA spots, based on spot features obtained in previous work by the authors. Despite certain theoretical advantages, Gaussian process ordinal regression failed to achieve any clear performance gain over classification using a multi-layer perceptron. The use of the entropy of the posterior probability distribution over class labels for avoiding uncertain decisions is demonstrated.