TY - GEN
T1 - Machine learning techniques and mammographic risk assessment
AU - Mac Parthaláin, Neil
AU - Zwiggelaar, Reyer
PY - 2010/7/21
Y1 - 2010/7/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77954626689&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13666-5_90
DO - 10.1007/978-3-642-13666-5_90
M3 - Conference Proceeding (Non-Journal item)
AN - SCOPUS:77954626689
SN - 9783642136658
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 664
EP - 672
BT - Digital Mammography - 10th International Workshop, IWDM 2010, Proceedings
A2 - Martí, J.
A2 - Oliver, A
A2 - Freixenet, J.
A2 - Martí, R.
T2 - 10th International Workshop on Digital Mammography, IWDM 2010
Y2 - 16 June 2010 through 18 June 2010
ER -
