Jussi Tohka
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Fast and robust parameter estimation for statistical partial volume models in brain MRI
J Tohka, A Zijdenbos, A Evans
Neuroimage 23 (1), 84-97, 2004
Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects
E Moradi, A Pepe, C Gaser, H Huttunen, J Tohka, ...
Neuroimage 104, 398-412, 2015
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge
EE Bron, M Smits, WM Van Der Flier, H Vrenken, F Barkhof, P Scheltens, ...
NeuroImage 111, 562-579, 2015
Automatic independent component labeling for artifact removal in fMRI
J Tohka, K Foerde, AR Aron, SM Tom, AW Toga, RA Poldrack
Neuroimage 39 (3), 1227-1245, 2008
Inter-subject correlation of brain hemodynamic responses during watching a movie: localization in space and frequency
JP Kauppi, IP Jääskeläinen, M Sams, J Tohka
Frontiers in neuroinformatics 4, 669, 2010
Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge
S Rueda, S Fathima, CL Knight, M Yaqub, AT Papageorghiou, ...
IEEE Transactions on medical imaging 33 (4), 797-813, 2013
Deconvolution-based partial volume correction in Raclopride-PET and Monte Carlo comparison to MR-based method
J Tohka, A Reilhac
Neuroimage 39 (4), 1570-1584, 2008
Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease
E Moradi, I Hallikainen, T Hänninen, J Tohka, ...
NeuroImage: Clinical 13, 415-427, 2017
Genetic algorithms for finite mixture model based voxel classification in neuroimaging
J Tohka, E Krestyannikov, ID Dinov, AMK Graham, DW Shattuck, ...
IEEE transactions on medical imaging 26 (5), 696-711, 2007
PET-SORTEO: validation and development of database of simulated PET volumes
A Reilhac, G Batan, C Michel, C Grova, J Tohka, DL Collins, N Costes, ...
IEEE Transactions on Nuclear Science 52 (5), 1321-1328, 2005
Inter-subject correlation in fMRI: method validation against stimulus-model based analysis
J Pajula, JP Kauppi, J Tohka
Public Library of Science 7 (8), e41196, 2012
Transfer learning in magnetic resonance brain imaging: a systematic review
JM Valverde, V Imani, A Abdollahzadeh, R De Feo, M Prakash, R Ciszek, ...
Journal of imaging 7 (4), 66, 2021
Prediction of brain maturity based on cortical thickness at different spatial resolutions
BS Khundrakpam, J Tohka, AC Evans, ...
Neuroimage 111, 350-359, 2015
Comparison of feature selection techniques in machine learning for anatomical brain MRI in dementia
J Tohka, E Moradi, H Huttunen, ...
Neuroinformatics 14, 279-296, 2016
Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review
J Tohka
World journal of radiology 6 (11), 855, 2014
Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data
E Moradi, B Khundrakpam, JD Lewis, AC Evans, J Tohka
Neuroimage 144, 128-141, 2017
Brain MRI tissue classification based on local Markov random fields
J Tohka, ID Dinov, DW Shattuck, AW Toga
Magnetic resonance imaging 28 (4), 557-573, 2010
A versatile software package for inter-subject correlation based analyses of fMRI
JP Kauppi, J Pajula, J Tohka
Frontiers in neuroinformatics 8, 2, 2014
Evaluation of machine learning algorithms for health and wellness applications: A tutorial
J Tohka, M Van Gils
Computers in Biology and Medicine 132, 104324, 2021
How many is enough? Effect of sample size in inter-subject correlation analysis of fMRI
J Pajula, J Tohka
Computational intelligence and neuroscience 2016, 2-2, 2016
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