| 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 | 857 | 2015 |
| 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 | 799 | 2004 |
| 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 | 441 | 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 | 284 | 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 | 264 | 2010 |
| 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 | 239 | 2017 |
| 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 | 198 | 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 | 195 | 2008 |
| 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 | 162 | 2021 |
| 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 | 158 | 2021 |
| 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 | 146 | 2007 |
| 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 | 122 | 2014 |
| 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 | 116 | 2012 |
| 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 | 112 | 2017 |
| 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 | 107 | 2005 |
| T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance JD Lewis, AC Evans, J Tohka, Brain Development Cooperative Group Neuroimage 173, 341-350, 2018 | 105 | 2018 |
| Comparison of feature selection techniques in machine learning for anatomical brain MRI in dementia J Tohka, E Moradi, H Huttunen, ... Neuroinformatics 14 (3), 279-296, 2016 | 105 | 2016 |
| Prediction of brain maturity based on cortical thickness at different spatial resolutions BS Khundrakpam, J Tohka, AC Evans, ... Neuroimage 111, 350-359, 2015 | 105 | 2015 |
| How many is enough? Effect of sample size in inter‐subject correlation analysis of fMRI J Pajula, J Tohka Computational intelligence and neuroscience 2016 (1), 2094601, 2016 | 101 | 2016 |
| Automated 3D axonal morphometry of white matter A Abdollahzadeh, I Belevich, E Jokitalo, J Tohka, A Sierra Scientific reports 9 (1), 6084, 2019 | 92 | 2019 |
Ulla RuotsalainenTampere UniversityVerified email at tuni.fi
