Machine learning techniques and mammographic risk assessment

Neil Mac Parthaláin*, Reyer Zwiggelaar

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)


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.

Iaith wreiddiolSaesneg
TeitlDigital Mammography - 10th International Workshop, IWDM 2010, Proceedings
GolygyddionJ. Martí, A Oliver, J. Freixenet, R. Martí
Nifer y tudalennau9
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 21 Gorff 2010
Digwyddiad10th International Workshop on Digital Mammography, IWDM 2010 - Girona, Catalonia, Sbaen
Hyd: 16 Meh 201018 Meh 2010

Cyfres gyhoeddiadau

EnwLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol6136 LNCS
ISSN (Argraffiad)0302-9743
ISSN (Electronig)1611-3349


Cynhadledd10th International Workshop on Digital Mammography, IWDM 2010
DinasGirona, Catalonia
Cyfnod16 Meh 201018 Meh 2010

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