no code implementations • 14 Mar 2024 • Geng Chen, Qingyue Wang, Islem Rekik
However, existing methods overlook the non-independent and identically distributed (non-IDD) issue stemming from multidomain brain connectivity heterogeneity, in which data domains are drawn from different hospitals and imaging modalities.
1 code implementation • 1 Jan 2024 • Michalis Pistos, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik
The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations.
no code implementations • 28 Dec 2023 • Ramona Ghilea, Islem Rekik
Furthermore, we leverage the hierarchical structure of the client network (both original and virtual), alongside the model diversity across replicas, and introduce a diversity-based tree aggregation, where replicas are combined in a tree-like manner and the aggregation weights are dynamically updated based on the model discrepancy.
no code implementations • 27 Dec 2023 • Christopher Adnel, Islem Rekik
Our three core contributions lie in (i) designing FALCON, a topology-aware graph reduction technique that preserves feature-label distribution; (ii) implementation of FALCON with other memory reduction methods (i. e., mini-batched GNN and quantization) for further memory reduction; (iii) extensive benchmarking and ablation studies against SOTA methods to evaluate FALCON memory reduction.
1 code implementation • 28 Oct 2023 • Bobby Azad, Reza Azad, Sania Eskandari, Afshin Bozorgpour, Amirhossein Kazerouni, Islem Rekik, Dorit Merhof
Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models.
no code implementations • 11 Aug 2023 • Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah, Alejandro F Frangi, Alena Buyx, Anais Emelie, Andrea Lara, Antonio R Porras, An-Wen Chan, Arcadi Navarro, Ben Glocker, Benard O Botwe, Bishesh Khanal, Brigit Beger, Carol C Wu, Celia Cintas, Curtis P Langlotz, Daniel Rueckert, Deogratias Mzurikwao, Dimitrios I Fotiadis, Doszhan Zhussupov, Enzo Ferrante, Erik Meijering, Eva Weicken, Fabio A González, Folkert W Asselbergs, Fred Prior, Gabriel P Krestin, Gary Collins, Geletaw S Tegenaw, Georgios Kaissis, Gianluca Misuraca, Gianna Tsakou, Girish Dwivedi, Haridimos Kondylakis, Harsha Jayakody, Henry C Woodruf, Hugo JWL Aerts, Ian Walsh, Ioanna Chouvarda, Irène Buvat, Islem Rekik, James Duncan, Jayashree Kalpathy-Cramer, Jihad Zahir, Jinah Park, John Mongan, Judy W Gichoya, Julia A Schnabel, Kaisar Kushibar, Katrine Riklund, Kensaku MORI, Kostas Marias, Lameck M Amugongo, Lauren A Fromont, Lena Maier-Hein, Leonor Cerdá Alberich, Leticia Rittner, Lighton Phiri, Linda Marrakchi-Kacem, Lluís Donoso-Bach, Luis Martí-Bonmatí, M Jorge Cardoso, Maciej Bobowicz, Mahsa Shabani, Manolis Tsiknakis, Maria A Zuluaga, Maria Bielikova, Marie-Christine Fritzsche, Marius George Linguraru, Markus Wenzel, Marleen de Bruijne, Martin G Tolsgaard, Marzyeh Ghassemi, Md Ashrafuzzaman, Melanie Goisauf, Mohammad Yaqub, Mohammed Ammar, Mónica Cano Abadía, Mukhtar M E Mahmoud, Mustafa Elattar, Nicola Rieke, Nikolaos Papanikolaou, Noussair Lazrak, Oliver Díaz, Olivier Salvado, Oriol Pujol, Ousmane Sall, Pamela Guevara, Peter Gordebeke, Philippe Lambin, Pieta Brown, Purang Abolmaesumi, Qi Dou, Qinghua Lu, Richard Osuala, Rose Nakasi, S Kevin Zhou, Sandy Napel, Sara Colantonio, Shadi Albarqouni, Smriti Joshi, Stacy Carter, Stefan Klein, Steffen E Petersen, Susanna Aussó, Suyash Awate, Tammy Riklin Raviv, Tessa Cook, Tinashe E M Mutsvangwa, Wendy A Rogers, Wiro J Niessen, Xènia Puig-Bosch, Yi Zeng, Yunusa G Mohammed, Yves Saint James Aquino, Zohaib Salahuddin, Martijn P A Starmans
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
no code implementations • 14 Dec 2022 • Oben Özgür, Arwa Rekik, Islem Rekik
Due to this reason, one-shot learning still remains one of the most challenging and trending concepts of deep learning as it proposes to simulate the human-like learning approach in classification problems.
no code implementations • 9 Dec 2022 • Doğa Türkseven, Islem Rekik, Christoph von Tycowicz, Martin Hanik
Predicting the future development of an anatomical shape from a single baseline observation is a challenging task.
1 code implementation • 14 Sep 2022 • Imen Jegham, Islem Rekik
However, state-of-the-art methods, on one hand, neglect the topological properties of the connectomes and, on the other hand, fail to solve the high inter-subject brain heterogeneity.
1 code implementation • 13 Sep 2022 • Mehmet Yigit Balik, Arwa Rekik, Islem Rekik
To the best of our knowledge, this presents the first work investigating the reproducibility of federated GNN models with application to classifying medical imaging and brain connectivity datasets.
no code implementations • 13 Sep 2022 • Ece Cinar, Sinem Elif Haseki, Alaa Bessadok, Islem Rekik
Our experiments show that from a single CBT, one can generate realistic connectomic datasets including brain graphs of varying resolutions and modalities.
1 code implementation • 13 Sep 2022 • Furkan Pala, Islem Rekik
A central challenge in training one-shot learning models is the limited representativeness of the available shots of the data space.
1 code implementation • 5 Apr 2022 • Nada Chaari, Hatice Camgoz Akdag, Islem Rekik
One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint.
1 code implementation • 26 Mar 2022 • Xuesong Wang, Lina Yao, Islem Rekik, Yu Zhang
Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored.
no code implementations • 24 Feb 2022 • Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, Dinggang Shen
Transformers have dominated the field of natural language processing, and recently impacted the computer vision area.
1 code implementation • 6 Oct 2021 • Umut Guvercin, Mohammed Amine Gharsallaoui, Islem Rekik
Using a one-representative CBT as a training sample, we alleviate the training load of GNN models while boosting their performance across a variety of classification and regression tasks.
1 code implementation • 6 Oct 2021 • Alpay Tekin, Ahmed Nebli, Islem Rekik
Several brain disorders can be detected by observing alterations in the brain's structural and functional connectivities.
1 code implementation • 6 Oct 2021 • Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik
To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs).
1 code implementation • 6 Oct 2021 • Oytun Demirbilek, Islem Rekik
To fill this gap, we propose Recurrent Multigraph Integrator Network (ReMI-Net), the first graph recurrent neural network which infers the baseline CBT of an input population t1 and predicts its longitudinal evolution over time (ti > t1).
1 code implementation • 6 Oct 2021 • Basar Demir, Alaa Bessadok, Islem Rekik
Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher.
1 code implementation • 6 Oct 2021 • Islem Mhiri, Mohamed Ali Mahjoub, Islem Rekik
Our SG-Net is grounded in three main contributions: (i) predicting a target graph from a source one based on a novel graph generative adversarial network in both inter (e. g., morphological-functional) and intra (e. g., functional-functional) domains, (ii) generating high-resolution brain graphs without resorting to the time consuming and expensive MRI processing steps, and (iii) enforcing the source distribution to match that of the ground truth graphs using an inter-modality aligner to relax the loss function to optimize.
1 code implementation • 16 Sep 2021 • Şeymanur Aktı, Doğay Kamar, Özgür Anıl Özlü, Ihsan Soydemir, Muhammet Akcan, Abdullah Kul, Islem Rekik
The competing teams developed their ML pipelines with a combination of data pre-processing, dimensionality reduction, and learning methods.
1 code implementation • 6 Sep 2021 • Mohammed Amine Gharsallaoui, Islem Rekik
While prior studies have focused on boosting the model accuracy, quantifying the reproducibility of the most discriminative features identified by GNNs is still an intact problem that yields concerns about their reliability in clinical applications in particular.
1 code implementation • 30 Jun 2021 • Islem Mhiri, Ahmed Nebli, Mohamed Ali Mahjoub, Islem Rekik
Our three core contributions lie in (i) predicting a target graph (e. g., functional) from a source graph (e. g., morphological) based on a novel graph generative adversarial network (gGAN); (ii) using non-isomorphic graphs for both source and target domains with a different number of nodes, edges and structure; and (iii) enforcing the predicted target distribution to match that of the ground truth graphs using a graph autoencoder to relax the designed loss oprimization.
1 code implementation • 17 Jun 2021 • Martin Hanik, Mehmet Arif Demirtaş, Mohammed Amine Gharsallaoui, Islem Rekik
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.
1 code implementation • 7 Jun 2021 • Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity.
1 code implementation • 6 May 2021 • Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
Brain graphs (i. e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain.
1 code implementation • 2 May 2021 • Megi Isallari, Islem Rekik
While typically the Graph U-Net is a node-focused architecture where graph embedding depends mainly on node attributes, we propose a graph-focused architecture where the node feature embedding is based on the graph topology.
1 code implementation • 4 Apr 2021 • Islem Rekik, Mustafa Burak Gurbuz
With the recent technological advances, biological datasets, often represented by networks (i. e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity.
2 code implementations • 28 Dec 2020 • Mustafa Burak Gurbuz, Islem Rekik
Particularly, estimating a well-centered and representative CBT for populations of multi-view brain networks (MVBN) is more challenging since these networks sit on complex manifolds and there is no easy way to fuse different heterogeneous network views.
1 code implementation • 28 Sep 2020 • Ahmed Nebli, Ugur Ali Kaplan, Islem Rekik
Our EvoGraphNet architecture cascades a set of time-dependent gGANs, where each gGAN communicates its predicted brain graphs at a particular timepoint to train the next gGAN in the cascade at follow-up timepoint.
1 code implementation • 24 Sep 2020 • Ahmed Nebli, Islem Rekik
Differently, in this paper, we tap into the nascent field of geometric-GANs (G-GAN) to design a deep multiplex prediction architecture comprising (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections and (ii) a conditional discriminator which classifies predicted target intra-layers by conditioning on the multiplex source intra-layers.
no code implementations • 24 Sep 2020 • Mustafa Saglam, Islem Rekik
The individual brain can be viewed as a highly-complex multigraph (i. e. a set of graphs also called connectomes), where each graph represents a unique connectional view of pairwise brain region (node) relationships such as function or morphology.
1 code implementation • 24 Sep 2020 • Alin Banka, Inis Buzi, Islem Rekik
For each subject, we further regularize the hypergraph autoencoding by adversarial regularization to align the distribution of the learned hyperconnectome embeddings with that of the input hyperconnectomes.
1 code implementation • 23 Sep 2020 • Ahmet Serkan Goktas, Alaa Bessadok, Islem Rekik
Next, to compute the similarity between subjects, we introduce the concept of a connectional brain template (CBT), a fixed network reference, where we further represent each training and testing network as a deviation from the reference CBT in the embedding space.
1 code implementation • 23 Sep 2020 • Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
Several works based on Generative Adversarial Networks (GAN) have been recently proposed to predict a set of medical images from a single modality (e. g, FLAIR MRI from T1 MRI).
1 code implementation • 23 Sep 2020 • Megi Isallari, Islem Rekik
Catchy but rigorous deep learning architectures were tailored for image super-resolution (SR), however, these fail to generalize to non-Euclidean data such as brain connectomes.
1 code implementation • 23 Sep 2020 • Zeynep Gurler, Ahmed Nebli, Islem Rekik
We use these embeddings to compute the similarity between training and testing subjects which allows us to pick the closest training subjects at baseline timepoint to predict the evolution of the testing brain graph over time.
2 code implementations • 23 Sep 2020 • Islem Mhiri, Mohamed Ali Mahjoub, Islem Rekik
Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field in mapping a population of brain networks in health and disease.
1 code implementation • 30 Aug 2020 • Mert Lostar, Islem Rekik
Graph embedding methods which map data samples (e. g., brain networks) into a low dimensional space have been widely used to explore the relationship between samples for classification or prediction tasks.
no code implementations • 5 Jun 2020 • Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Stewart H. Mostofsky, Mary Beth Nebel, Keri Rosch, Karen Seymour, Deana Crocetti, Hassna Irzan, Michael Hütel, Sebastien Ourselin, Neil Marlow, Andrew Melbourne, Egor Levchenko, Shuo Zhou, Mwiza Kunda, Haiping Lu, Nicha C. Dvornek, Juntang Zhuang, Gideon Pinto, Sandip Samal, Jennings Zhang, Jorge L. Bernal-Rusiel, Rudolph Pienaar, Ai Wern Chung
A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing.
1 code implementation • 28 Apr 2020 • Ismail Bilgen, Goktug Guvercin, Islem Rekik
Indeed, machine learning (ML) studies for ASD diagnosis using morphological brain networks derived from conventional T1-weighted MRI are very scarce.
no code implementations • 13 Jul 2019 • Can Gafuroglu, Islem Rekik
To this aim, we propose novel supervised and unsupervised frameworks that learn how to jointly predict and label the evolution trajectory of intensity patches, each seeded at a specific brain landmark, from a baseline intensity patch.
no code implementations • 6 Aug 2018 • Mayssa Soussia, Islem Rekik
Unveiling pathological brain changes associated with Alzheimer's disease (AD) is a challenging task especially that people do not show symptoms of dementia until it is late.
no code implementations • 5 Sep 2017 • Samya Amiri, Mohamed Ali Mahjoub, Islem Rekik
Unlike previous works that simply aggregate or cascade classifiers for addressing image segmentation and labeling tasks, we propose to embed strong classifiers into a tree structure that allows bi-directional flow of information between its classifier nodes to gradually improve their performances.