Machine learning techniques and mammographic risk assessment

Neil Mac Parthaláin*, Reyer Zwiggelaar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

5 Citations (SciVal)

Abstract

Breast tissue characteristics are widely accepted as important indicators of the likelihood of the developing breast cancer. Methods which have the ability to automatically classify breast tissue distribution therefore provide important tools in assessing the risk to which patients are exposed. This paper examines the machine learning techniques employed for knowledge discovery in a recent approach to mammographic risk assessment. A number of weaknesses for selected classification techniques are identified and examined. Additionally, important trends in the data such as decision class confusion and how this affects the ability to perform accurate knowledge discovery on the extracted image data are also explored. The paper is concluded with some ideas as to how the identified trends in the data and weaknesses in the classification approaches could be addressed.

Original languageEnglish
Title of host publicationDigital Mammography - 10th International Workshop, IWDM 2010, Proceedings
EditorsJ. Martí, A Oliver, J. Freixenet, R. Martí
Pages664-672
Number of pages9
DOIs
Publication statusPublished - 21 Jul 2010
Event10th International Workshop on Digital Mammography, IWDM 2010 - Girona, Catalonia, Spain
Duration: 16 Jun 201018 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6136 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Digital Mammography, IWDM 2010
Country/TerritorySpain
CityGirona, Catalonia
Period16 Jun 201018 Jun 2010

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