Brain And SIgnal Research & Analysis laboratory

Develop your research skills in brain data learning
“My training has given me the tools and the confidence to understand and critically assess the contents of research papers so that I can direct my own research.”
Can Gafuroğlu (Undergrad student)

Applications for internships are open now.


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 gets affected by neuropsychiatric or neurodegenerative disorders. 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

Islem Rekik, Ph.D.


Olfa Ghribi's paper on multi-regression based supervised sample selection for predicting baby connectome evolution trajectory from neonatal timepoint is accepted for publication in the journal of Medical Image Analysis (IF: 11.14)!


September 14, 2020

Ahmed Nebli's paper on adversarial brain multiplex prediction from a single brain network for gender fingerprinting is accepted for publication in the journal of Medical Image Analysis (IF: 11.14)!


September 7, 2020

We have 9 papers accepted for publication in the LNCS Springer proceedings of MICCAI 2020 workshops (PRIME, MLMI and GRAIL)! 7 out of 9 are from ITU undergraduate students.

Congratulations everyone!

August 5, 2020

We have 3 papers accepted at MICCAI 2020!

Congratulations to Alaa, Islem, and Burak (early accept)!

June 23, 2020

Nicolas and Islem's paper on identifying the best data-driven feature selection method for boosting reproducibility in classification tasks is accepted for publication in the journal of Pattern Recognition (IF: 5.89)!


December 24, 2019


If you are interested, please contact us and send your CV and letter of motivation.

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