Alaa Bessadok

Alaa Bessadok
University of Sousse | ISTLS · Department of Computer Science

PhD student

About

15
Publications
1,439
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29
Citations

Publications

Publications (15)
Preprint
Full-text available
Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of...
Preprint
Full-text available
Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-consuming preprocessing pipeline of neuroimaging data. However, existing deep learning frameworks are solely designed to predict high-resolution (HR) image from a low-reso...
Chapter
Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of...
Chapter
Full-text available
Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-consuming preprocessing pipeline of neuroimaging data. However, existing deep learning frameworks are solely designed to predict high-resolution (HR) image from a low-reso...
Preprint
Full-text available
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, grap...
Preprint
Full-text available
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. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (G...
Article
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. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (G...
Article
Full-text available
Developing predictive intelligence in neuroscience for learning how to generate multimodality medical data from a single modality can improve neurological disorder diagnosis with minimal data acquisition resources. Existing deep learning frameworks are mainly tailored for images, which might fail in handling geometric data (e.g., brain graphs). Spe...
Chapter
Predicting the evolution trajectories of brain data from a baseline timepoint is a challenging task in the fields of neuroscience and neuro-disorders. While existing predictive frameworks are able to handle Euclidean structured data (i.e, brain images), they might fail to generalize to geometric non-Euclidean data such as brain networks. Recently,...
Preprint
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). However, such frameworks are primarily designed to operate on images, limiting their generalizability to non-Euclidean geometric data such as brain graphs. While a growing n...
Preprint
Full-text available
While existing predictive frameworks are able to handle Euclidean structured data (i.e, brain images), they might fail to generalize to geometric non-Euclidean data such as brain networks. Besides, these are rooted the sample selection step in using Euclidean or learned similarity measure between vectorized training and testing brain networks. Such...
Chapter
Multimodal medical datasets with incomplete observations present a barrier to large-scale neuroscience studies. 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). However, such frameworks are primarily designed to operate o...
Chapter
Full-text available
Recently, deep learning methods have been widely used for medical data synthesis. However, existing deep learning frameworks are mainly designed to predict Euclidian structured data (i.e., image), which causes them to fail when handling geometric data (e.g., brain graphs). Besides, these do not naturally account for domain fracture between training...
Chapter
Medical image synthesis techniques can circumvent the need for costly clinical scan acquisitions using different modalities such as functional Magnetic Resonance Imaging (MRI). Recently, deep learning frameworks were designed to predict a target medical modality from a source one (e.g., MRI from Computed Tomography (CT)). However, such methods whic...
Chapter
The morphology of anatomical brain regions can be affected by neurological disorders, including dementia and schizophrenia, to various degrees. Hence, identifying the morphological signature of a specific brain disorder can improve diagnosis and better explain how neuroanatomical changes associate with function and cognition. To capture this signat...

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