@inproceedings{TuerksevenRekikvonTycowiczetal., author = {T{\"u}rkseven, Doğa and Rekik, Islem and von Tycowicz, Christoph and Hanik, Martin}, title = {Predicting Shape Development: A Riemannian Method}, series = {Shape in Medical Imaging}, booktitle = {Shape in Medical Imaging}, publisher = {Springer Nature}, doi = {10.1007/978-3-031-46914-5_17}, pages = {211 -- 222}, abstract = {Predicting the future development of an anatomical shape from a single baseline observation is a challenging task. But it can be essential for clinical decision-making. Research has shown that it should be tackled in curved shape spaces, as (e.g., disease-related) shape changes frequently expose nonlinear characteristics. We thus propose a novel prediction method that encodes the whole shape in a Riemannian shape space. It then learns a simple prediction technique founded on hierarchical statistical modeling of longitudinal training data. When applied to predict the future development of the shape of the right hippocampus under Alzheimer's disease and to human body motion, it outperforms deep learning-supported variants as well as state-of-the-art.}, language = {en} } @article{HanikDemirtaşGharsallaouietal., author = {Hanik, Martin and Demirta{\c{s}}, Mehmet Arif and Gharsallaoui, Mohammed Amine and Rekik, Islem}, title = {Predicting cognitive scores with graph neural networks through sample selection learning}, series = {Brain Imaging and Behavior}, volume = {16}, journal = {Brain Imaging and Behavior}, doi = {10.1007/s11682-021-00585-7}, pages = {1123 -- 1138}, abstract = {Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In existing literature, functional brain connectomes have been used successfully to predict cognitive measures such as intelligence quotient (IQ) scores in both healthy and disordered cohorts using machine learning models. However, existing methods resort to flattening the brain connectome (i.e., graph) through vectorization which overlooks its topological properties. To address this limitation and inspired from the emerging graph neural networks (GNNs), we design a novel regression GNN model (namely RegGNN) for predicting IQ scores from brain connectivity. On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task. However, since such deep learning architectures are computationally expensive to train, we further propose a \emph{learning-based sample selection} method that learns how to choose the training samples with the highest expected predictive power on unseen samples. For this, we capitalize on the fact that connectomes (i.e., their adjacency matrices) lie in the symmetric positive definite (SPD) matrix cone. Our results on full-scale and verbal IQ prediction outperforms comparison methods in autism spectrum disorder cohorts and achieves a competitive performance for neurotypical subjects using 3-fold cross-validation. Furthermore, we show that our sample selection approach generalizes to other learning-based methods, which shows its usefulness beyond our GNN architecture.}, language = {en} }