Classification of MRI under the Presence of Disease Heterogeneity using Multi-Task Learning: Application to Bipolar Disorder
Carregando...
Citações na Scopus
Tipo de produção
conferenceObject
Data de publicação
2015
Título da Revista
ISSN da Revista
Título do Volume
Editora
SPRINGER INT PUBLISHING AG
Autores
Citação
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2015, PT I, v.9349, p.125-132, 2015
Resumo
Heterogeneity in psychiatric and neurological disorders has undermined our ability to understand the pathophysiology underlying their clinical manifestations. In an effort to better distinguish clinical subtypes, many disorders, such as Bipolar Disorder, have been further sub-categorized into subgroups, albeit with criteria that are not very clear, reproducible and objective. Imaging, along with pattern analysis and classification methods, offers promise for developing objective and quantitative ways for disease subtype categorization. Herein, we develop such a method using learning multiple tasks, assuming that each task corresponds to a disease subtype but that subtypes share some common imaging characteristics, along with having distinct features. In particular, we extend the original SVM method by incorporating the sparsity and the group sparsity techniques to allow simultaneous joint learning for all diagnostic tasks. Experiments on Multi-Task Bipolar Disorder classification demonstrate the advantages of our proposed methods compared to other state-of-art pattern analysis approaches.
Palavras-chave
Referências
- Caruana R, 1997, MACH LEARN, V28, P41, DOI 10.1023/A:1007379606734
- Zhou JY, 2013, NEUROIMAGE, V78, P233, DOI 10.1016/j.neuroimage.2013.03.073
- Yuan M, 2006, J ROY STAT SOC B, V68, P49, DOI 10.1111/j.1467-9868.2005.00532.x
- Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267
- Simon N, 2013, J COMPUT GRAPH STAT, V22, P231, DOI 10.1080/10618600.2012.681250
- Metsis V, 2014, IEEE ACM T COMPUT BI, V11, P168, DOI 10.1109/TCBB.2013.141
- DUNNER DL, 1976, BIOL PSYCHIAT, V11, P31
- Wang H, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P557
- Azadi S., 2014, ICML, P620
- Cai X., 2011, ICDM, P91
- Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI 10.1145/1961189.1961199
- Evgeniou A., 2007, NIPS, P41
- Jie B, 2015, HUM BRAIN MAPP, V36, P489, DOI 10.1002/hbm.22642
- Nie F., 2010, NIPS, P1813
- Rao N., 2013, NIPS, P2202
- Scholkopf B, 2002, LEARNING KERNELS SUP
- Serpa M.H., 2014, BIOMED RES INT