DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

no code implementations • 18 Nov 2023 • Vladimir Belov, Tracy Erwin-Grabner, Ling-Li Zeng, Christopher R. K. Ching, Andre Aleman, Alyssa R. Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Colm G. Connolly, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Kathryn Cullen, Udo Dannlowski, Christopher G. Davey, Annemiek Dols, Jan Ernsting, Jennifer W. Evans, Lukas Fisch, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Hans J. Grabe, Nynke A. Groenewold, Dominik Grotegerd, Tim Hahn, J. Paul Hamilton, Laura K. M. Han, Ben J Harrison, Tiffany C. Ho, Neda Jahanshad, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Ramona Leenings, Meng Li, David E. J. Linden, Frank P. MacMaster, David M. A. Mehler, Susanne Meinert, Elisa Melloni, Bryon A. Mueller, Benson Mwangi, Igor Nenadić, Amar Ojha, Yasumasa Okamoto, Mardien L. Oudega, Brenda W. J. H. Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J. Portella, Elena Pozzi, Joaquim Radua, Elena Rodríguez-Cano, Matthew D. Sacchet, Raymond Salvador, Anouk Schrantee, Kang Sim, Jair C. Soares, Aleix Solanes, Dan J. Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Yolanda Vives-Gilabert, Henry Völzke, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils Winter, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado

Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

no code implementations • 16 Jun 2022 • Vladimir Belov, Tracy Erwin-Grabner, Ali Saffet Gonul, Alyssa R. Amod, Amar Ojha, Andre Aleman, Annemiek Dols, Anouk Scharntee, Aslihan Uyar-Demir, Ben J Harrison, Benson M. Irungu, Bianca Besteher, Bonnie Klimes-Dougan, Brenda W. J. H. Penninx, Bryon A. Mueller, Carlos Zarate, Christopher G. Davey, Christopher R. K. Ching, Colm G. Connolly, Cynthia H. Y. Fu, Dan J. Stein, Danai Dima, David E. J. Linden, David M. A. Mehler, Edith Pomarol-Clotet, Elena Pozzi, Elisa Melloni, Francesco Benedetti, Frank P. MacMaster, Hans J. Grabe, Henry Völzke, Ian H. Gotlib, Jair C. Soares, Jennifer W. Evans, Kang Sim, Katharina Wittfeld, Kathryn Cullen, Liesbeth Reneman, Mardien L. Oudega, Margaret J. Wright, Maria J. Portella, Matthew D. Sacchet, Meng Li, Moji Aghajani, Mon-Ju Wu, Natalia Jaworska, Neda Jahanshad, Nic J. A. van der Wee, Nynke Groenewold, Paul J. Hamilton, Philipp Saemann, Robin Bülow, Sara Poletti, Sarah Whittle, Sophia I. Thomopoulos, Steven J. A. van, der Werff, Sheri-Michelle Koopowitz, Thomas Lancaster, Tiffany C. Ho, Tony T. Yang, Zeynep Basgoze, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders.

Classification

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations • 4 Jul 2020 • Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model

no code implementations • 26 May 2017 • Dajiang Zhu, Brandalyn C. Riedel, Neda Jahanshad, Nynke A. Groenewold, Dan J. Stein, Ian H. Gotlib, Matthew D. Sacchet, Danai Dima, James H. Cole, Cynthia H. Y. Fu, Henrik Walter, Ilya M. Veer, Thomas Frodl, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson

Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature.

Classification feature selection +1