TY - GEN
T1 - Learning with heterogeneous data for longitudinal studies
AU - Squarcina, Letizia
AU - Perlini, Cinzia
AU - Bellani, Marcella
AU - Lasalvia, Antonio
AU - Ruggeri, Mirella
AU - Brambilla, Paolo
AU - Castellani, Umberto
PY - 2015
Y1 - 2015
N2 - Longitudinal studies are very important to understand cerebral structural changes especially during the course of pathologies. For instance, in the context of mental health research, it is interesting to evaluate how a certain disease degenerates over time in order to discriminate between pathological and normal time dependent brain deformations. However longitudinal data are not easily available, and very often they are characterized by a large variability in both the age of subjects and time between acquisitions (follow up time). This leads to heterogeneous data that may affect the overall study. In this paper we propose a learning method to deal with this kind of heterogeneous data by exploiting covariate measures in a Multiple Kernel Learning (MKL) framework. Cortical thickness and white matter volume of the left middle temporal region are collected from each subject. Then, a subject-dependent kernel weighting procedure is introduced in order to obtain the correction of covariate effect simultaneously with classification. Experiments are reported for First Episode Psychosis detection by showing very promising results.
AB - Longitudinal studies are very important to understand cerebral structural changes especially during the course of pathologies. For instance, in the context of mental health research, it is interesting to evaluate how a certain disease degenerates over time in order to discriminate between pathological and normal time dependent brain deformations. However longitudinal data are not easily available, and very often they are characterized by a large variability in both the age of subjects and time between acquisitions (follow up time). This leads to heterogeneous data that may affect the overall study. In this paper we propose a learning method to deal with this kind of heterogeneous data by exploiting covariate measures in a Multiple Kernel Learning (MKL) framework. Cortical thickness and white matter volume of the left middle temporal region are collected from each subject. Then, a subject-dependent kernel weighting procedure is introduced in order to obtain the correction of covariate effect simultaneously with classification. Experiments are reported for First Episode Psychosis detection by showing very promising results.
KW - First Episode Psychosis
KW - Longitudinal study
KW - Multiple Kernel Learning
KW - Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=84951815839&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84951815839&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24574-4_64
DO - 10.1007/978-3-319-24574-4_64
M3 - Conference contribution
AN - SCOPUS:84951815839
SN - 9783319245737
VL - 9351
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 535
EP - 542
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Y2 - 5 October 2015 through 9 October 2015
ER -