Journal: NeuroImage : Clinical

Article Title: 3D scattering transforms for disease classification in neuroimaging

doi: 10.1016/j.nicl.2017.02.004

Figure Lengend Snippet: A scheme of the main steps of the 3D scattering paradigm. A procedure to regress out the effect of age on the MRI scans, is performed in the beginning. The 3D scattering transform is subsequently applied to each MRI subject resulting in the scattering representation of the MRI data. Cross-validation is performed in all the experiments where data are split as follows: 60% training + 20% validation + 20% test. Supervised learning is performed using a linear support vector machines (SVM) classifier. The learning algorithm used in the last experiment, which is a semi-supervised learning experiment performed on the ADNI dataset, is a Laplacian SVM. Not shown is the introduced method of visualization of evidence for or against a class, on both the group and individual level, see Section 2.5 .

Article Snippet: Code for Laplacian SVM used in our experiments is available at http://sourceforge.net/projects/lapsvmp/

Techniques: Biomarker Discovery, Plasmid Preparation

Journal: NeuroImage : Clinical

Article Title: 3D scattering transforms for disease classification in neuroimaging

doi: 10.1016/j.nicl.2017.02.004

Figure Lengend Snippet: Results of the MCI experiments. The first experiment is a supervised learning experiment with two labels, pMCI and sMCI. Using a linear SVM on top of the scattering representation leads to a superior classification accuracy compared to state-of-the-art by Moradi et al. (2015) . The second experiment is an SSL experiment where the training data consist of unlabeled MCI MRI data in addition to data belonging to the pMCI and sMCI labels. Using a Laplacian SVM trained in the primal on top of the scattering representation leads to a higher accuracy than state-of-the-art by Moradi et al. (2015) .

Article Snippet: Code for Laplacian SVM used in our experiments is available at http://sourceforge.net/projects/lapsvmp/

Techniques:

Journal: Sn Computer Science

Article Title: A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy

doi: 10.1007/s42979-022-01358-9

Figure Lengend Snippet: Summary of machine learning in epilepsy

Article Snippet: The second technique was the phase-space Laplacian spectrum, which used the metrics of the phase-space Laplacian spectrum as a biomarker of seizure prediction; this achieved 93% accuracy.

Techniques: Plasmid Preparation