Publications

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2023
Bibars, M., P. E. Salah, A. Eldeib, M. A. Elattar, and I. A. Yassine, "Cross-Modality Deep Transfer Learning: Application to Liver Segmentation in CT and MRI", Annual Conference on Medical Image Understanding and Analysis, UK, pp. 96-110, June, 2023.
Mousa, D., N. Zayed, and I. A. Yassine, "Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data.", Scientific reports, vol. 13, issue 1, pp. 21559, 2023. Abstract

Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.

2022
Khder, S. M., E. A. H. Mohamed, and I. A. Yassine, "A Clustering- based Fusion System for Blastomere Localization", Biomedical Engineering: Applications, Basis and Communications, vol. 34, issue 4, pp. 2250021, 2022.
Khedr, S. M., E. A. H. Mohamed, and I. A. Yassine, "A Clustering-based Fusion System for Blastomere Localization", Biomedical Engineering: Applications, Basis and Communications, pp. 2250021, 2022.
2018
Yassine, I. A., W. M. Eldeib, K. A. Gad, Y. A. Ashour, I. A. Yassine, and A. O. Hosy, "Cognitive functions, electroencephalographic and diffusion tensor imaging changes in children with active idiopathic epilepsy", Epilepsy & Behavior, vol. 84, pp. 135-141, 2018.
2016
Khalaf, A. F., I. A. Yassine, and A. S. Fahmy, "Convolutional neural networks for deep feature learning in retinal vessel segmentation ", IEEE International Conference on Image Processing (ICIP), Phoenix, USA, Sept, 2016.
2015
Yassine, I. A., M. L. Serrano, B. Vila, J. M. Tormos, and E. Gomez, "Content-based image retrieval system for brain magnetic resonance imaging based on Pseudo-Zernike coefficients combined with wavelet features", Computer Assisted Radiology and Surgery, Barcelona, Spain, 2015.
2014
Serag, M., M. Wael, I. A. Yassine, and A. S. Fahmy, "Cardiac MRI View Classification using Autoencoder", Cairo International Biomedical Engineering Conference, Cairo, Egypt, pp. 125-128, 2014.
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