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Items: 1 to 20 of 166

1.

Folded concave penalized learning in identifying multimodal MRI marker for Parkinson's disease.

Liu H, Du G, Zhang L, Lewis MM, Wang X, Yao T, Li R, Huang X.

J Neurosci Methods. 2016 Aug 1;268:1-6. doi: 10.1016/j.jneumeth.2016.04.016. Epub 2016 Apr 19.

2.

Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data.

Adeli E, Shi F, An L, Wee CY, Wu G, Wang T, Shen D.

Neuroimage. 2016 Nov 1;141:206-219. doi: 10.1016/j.neuroimage.2016.05.054. Epub 2016 Jun 10.

3.

A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI.

Haq NF, Kozlowski P, Jones EC, Chang SD, Goldenberg SL, Moradi M.

Comput Med Imaging Graph. 2015 Apr;41:37-45. doi: 10.1016/j.compmedimag.2014.06.017. Epub 2014 Jul 5.

4.

Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging.

Cantor-Rivera D, Khan AR, Goubran M, Mirsattari SM, Peters TM.

Comput Med Imaging Graph. 2015 Apr;41:14-28. doi: 10.1016/j.compmedimag.2014.07.002. Epub 2014 Jul 21.

PMID:
25103878
5.

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

Peng B, Wang S, Zhou Z, Liu Y, Tong B, Zhang T, Dai Y.

Neurosci Lett. 2017 Jun 9;651:88-94. doi: 10.1016/j.neulet.2017.04.034. Epub 2017 Apr 21.

PMID:
28435046
6.

Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation.

Akhbardeh A, Jacobs MA.

Med Phys. 2012 Apr;39(4):2275-89. doi: 10.1118/1.3682173.

7.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

Salvatore C, Cerasa A, Castiglioni I, Gallivanone F, Augimeri A, Lopez M, Arabia G, Morelli M, Gilardi MC, Quattrone A.

J Neurosci Methods. 2014 Jan 30;222:230-7. doi: 10.1016/j.jneumeth.2013.11.016. Epub 2013 Nov 26.

PMID:
24286700
8.

Combined unsupervised-supervised classification of multiparametric PET/MRI data: application to prostate cancer.

Gatidis S, Scharpf M, Martirosian P, Bezrukov I, Küstner T, Hennenlotter J, Kruck S, Kaufmann S, Schraml C, la Fougère C, Schwenzer NF, Schmidt H.

NMR Biomed. 2015 Jul;28(7):914-22. doi: 10.1002/nbm.3329. Epub 2015 May 26.

PMID:
26014883
9.

Multi-modality canonical feature selection for Alzheimer's disease diagnosis.

Zhu X, Suk HI, Shen D.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):162-9.

10.

Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.

Ginsburg SB, Viswanath SE, Bloch BN, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A.

J Magn Reson Imaging. 2015 May;41(5):1383-93. doi: 10.1002/jmri.24676. Epub 2014 Jun 18.

PMID:
24943647
11.

Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease.

Cao P, Liu X, Liu H, Yang J, Zhao D, Huang M, Zaiane O.

Comput Methods Programs Biomed. 2018 Aug;162:19-45. doi: 10.1016/j.cmpb.2018.04.028. Epub 2018 May 3.

PMID:
29903486
12.

Automatic Classification on Multi-Modal MRI Data for Diagnosis of the Postural Instability and Gait Difficulty Subtype of Parkinson's Disease.

Gu Q, Zhang H, Xuan M, Luo W, Huang P, Xia S, Zhang M.

J Parkinsons Dis. 2016 May 11;6(3):545-56. doi: 10.3233/JPD-150729.

PMID:
27176623
13.

Sparse feature learning for multi-class Parkinson's disease classification.

Lei H, Zhao Y, Wen Y, Luo Q, Cai Y, Liu G, Lei B.

Technol Health Care. 2018;26(S1):193-203. doi: 10.3233/THC-174548.

14.

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.

Dyrba M, Grothe M, Kirste T, Teipel SJ.

Hum Brain Mapp. 2015 Jun;36(6):2118-31. doi: 10.1002/hbm.22759. Epub 2015 Feb 9.

PMID:
25664619
15.

Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme.

Fathi Kazerooni A, Mohseni M, Rezaei S, Bakhshandehpour G, Saligheh Rad H.

MAGMA. 2015 Feb;28(1):13-22. doi: 10.1007/s10334-014-0442-7. Epub 2014 Apr 2.

PMID:
24691860
16.

Feature analysis for Parkinson's disease detection based on transcranial sonography image.

Chen L, Hagenah J, Mertins A.

Med Image Comput Comput Assist Interv. 2012;15(Pt 3):272-9.

PMID:
23286140
17.

Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer's disease.

Jie B, Zhang D, Cheng B, Shen D.

Med Image Comput Comput Assist Interv. 2013;16(Pt 1):275-83.

18.

Image manifold revealing for breast lesion segmentation in DCE-MRI.

Hu L, Cheng Z, Wang M, Song Z.

Biomed Mater Eng. 2015;26 Suppl 1:S1353-60. doi: 10.3233/BME-151433.

PMID:
26405896
19.

Automatic extraction of the cingulum bundle in diffusion tensor tract-specific analysis: feasibility study in Parkinson's disease with and without dementia.

Ito K, Masutani Y, Kamagata K, Yasmin H, Suzuki Y, Ino K, Aoki S, Kunimatsu A, Ohtomo K.

Magn Reson Med Sci. 2013;12(3):201-13. Epub 2013 Jul 12.

20.

Locally linear embedding (LLE) for MRI based Alzheimer's disease classification.

Liu X, Tosun D, Weiner MW, Schuff N; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2013 Dec;83:148-57. doi: 10.1016/j.neuroimage.2013.06.033. Epub 2013 Jun 21.

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