Scoring of breast tissue microarray spots through ordinal regression

Telmo Amaral; Stephen J. McKenna; Katherine Robertson; Alastair Thompson

Scoring of breast tissue microarray spots through ordinal regression

Telmo Amaral, Stephen J. McKenna, Katherine Robertson, Alastair Thompson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, Vol 2
Place of Publication	Lisbon
Publisher	Institute for Systems and Technologies of Information, Control and Communication
Pages	243-248
Number of pages	6
Volume	2
ISBN (Print)	9789898111692
Publication status	Published - 2009
Event	4th International Conference on Computer Vision Theory and Applications - Lisboa, Lisbon, Portugal Duration: 5 Feb 2009 → 8 Feb 2009 http://www.visapp.visigrapp.org/VISAPP2009/

Conference

Conference	4th International Conference on Computer Vision Theory and Applications
Abbreviated title	VISAPP 2009
Country/Territory	Portugal
City	Lisbon
Period	5/02/09 → 8/02/09
Internet address	http://www.visapp.visigrapp.org/VISAPP2009/

Keywords

Breast tissue microarrays
Scoring
Immunohistochemistry
Ordinal regression
Validation
Carcinoma
Grade

Cite this

@inproceedings{e79ceb049f1d47bc83812cb5b92c6a26,

title = "Scoring of breast tissue microarray spots through ordinal regression",

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.",

keywords = "Breast tissue microarrays, Scoring, Immunohistochemistry, Ordinal regression, Validation, Carcinoma, Grade",

author = "Telmo Amaral and McKenna, {Stephen J.} and Katherine Robertson and Alastair Thompson",

year = "2009",

language = "English",

isbn = "9789898111692",

volume = "2",

pages = "243--248",

booktitle = "Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, Vol 2",

publisher = "Institute for Systems and Technologies of Information, Control and Communication",

note = "4th International Conference on Computer Vision Theory and Applications, VISAPP 2009 ; Conference date: 05-02-2009 Through 08-02-2009",

url = "http://www.visapp.visigrapp.org/VISAPP2009/",

}

Amaral, T, McKenna, SJ, Robertson, K & Thompson, A 2009, Scoring of breast tissue microarray spots through ordinal regression. in Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, Vol 2. vol. 2, Institute for Systems and Technologies of Information, Control and Communication, Lisbon, pp. 243-248, 4th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, 5/02/09.

Scoring of breast tissue microarray spots through ordinal regression. / Amaral, Telmo; McKenna, Stephen J.; Robertson, Katherine et al.
Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, Vol 2. Vol. 2 Lisbon: Institute for Systems and Technologies of Information, Control and Communication, 2009. p. 243-248.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Scoring of breast tissue microarray spots through ordinal regression

AU - Amaral, Telmo

AU - McKenna, Stephen J.

AU - Robertson, Katherine

AU - Thompson, Alastair

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Breast tissue microarrays

KW - Scoring

KW - Immunohistochemistry

KW - Ordinal regression

KW - Validation

KW - Carcinoma

KW - Grade

M3 - Conference contribution

SN - 9789898111692

VL - 2

SP - 243

EP - 248

BT - Visapp 2009: Proceedings of The Fourth International Conference on Computer Vision Theory and Applications, Vol 2

PB - Institute for Systems and Technologies of Information, Control and Communication

CY - Lisbon

T2 - 4th International Conference on Computer Vision Theory and Applications

Y2 - 5 February 2009 through 8 February 2009

ER -

Scoring of breast tissue microarray spots through ordinal regression

Abstract

Conference

Keywords

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