Brain And SIgnal Research & Analysis laboratory
BASIRA (Brain And SIgnal Research & Analysis) aims to infuse advanced computer vision and machine-learning methods into big neuroimaging and signal data analysis for improving healthcare and wellbeing. Specifically, since one can look at the brain as an image, a shape, or a connectional network, we aspire to develop advanced image-based, shape-based, and network-based medical data analysis techniques, that will provide a foundation for better understanding normal brain development and ageing, as well as how the brain (image, shape and network) gets affected by neuropsychiatric diseases such as autism or neurodegenerative disorders such as dementia. We aim to develop algorithms and architectures for mapping the healthy brain and computer-aided tools for examining the diseased/disordered brain.
More broadly, we also aim to devise efficient algorithms that perform several medical image and signal analysis tasks such as segmentation/labeling, registration, prediction, classification and regression.
News and Events
Congratulations to Can Gafuroğlu for winning the Young Software Engineer of Scotland! Can’s work on “Joint prediction and Classification of
Subscribe to our BASIRA Lab YouTube channel & watch our videos! To follow our recent published research works, you
6 papers accepted at MICCAI workshops (MLMI, PRIME, CNI) 2018! For more information about our accepted papers in the main
CNI-MICCAI: The second workshop on “Connectomics in NeuroImaging” at MICCAI, Granada 2018! We’re organizing the second workshop on “Connectomics in
PRIME-MICCAI: The first workshop on “PRedictive Intelligence in MEdicine” at MICCAI, Granada 2018! We’re organizing the first workshop on “PRedictive
Islem Rekik, Ph.D.
Assistant Professor at İTÜ Faculty of Computer and Informatics Engineering
Director, BASIRA Lab
Honorary researcher at
School of Science and Engineering Department of Computing, University of Dundee
For applications, please send your CV to email@example.com
Nesrine's paper on cross-view self-similarity using shared dictionary learning for cervical cancer staging is accepted for publication in the Journal of IEEE Access. Congratulations!
February 19, 2019
Salma's paper on estimating brain network atlases for healthy and disordered populations is accepted for publication in the Journal of Neuroscience Methods. Congratulations!
September 25, 2018
Mayssa's paper on unsupervised manifold learning using high-order morphological brain networks derived from T1-w MRI is accepted for publication in Frontiers in Neuroinformatics journal. Congratulations!
September 20, 2018
Rory's paper on multi-view morphological brain multiplex for early mild cognitive impairment diagnosis is accepted for publication in IEEE Access journal. Congratulations!
July 28, 2018
Nicolas's paper on data-specific feature selection method identification for most reproducible connectomic feature discovery is accepted for publication in CNI-MICCAI2018 LNCS Springer proceedings. Congratulations!
July 20, 2018
Alaa's paper on intact connectional brain morphometricy for brain disorder fingerprint identification is accepted for publication in CNI-MICCAI2018 LNCS Springer proceedings. Congratulations!
July 20, 2018
Nesrine's paper on dynamic multi-scale CNN forest learning for cervical cancer segmentation is accepted for publication in MLMI-MICCAI2018 LNCS Springer proceedings. Congratulations!
July 19, 2018
Anna's paper on decoding emotional intelligence brain construct using functional connectomic data is accepted for publication in PRIME-MICCAI2018 LNCS Springer proceedings. Congratulations!
July 15, 2018
Minghui's paper on predicting target multi-view brain networks from a source view is accepted for publication in PRIME-MICCAI 2018 LNCS Springer proceedings. Congratulations!
July 15, 2018
Anna's paper on joint pairing and structured mapping of convolutional brain morphological multiplexes for early dementia diagnosis is accepted for publication in Brain Connectivity. Congratulations!
June 21, 2018