Faculty Office Ext.
Mustafa Elattar received his bachelor’s degree in Systems and Biomedical Engineering in 2008. Mustafa joined the Medical Imaging and Image Processing research group at Nile University as a research assistant. In 2010, he obtained his master’s degree in Communication and Information Technology after conducting image analysis research for cardiac imaging. He received his Ph.D. in Biomedical Engineering and Physics, Medicine in 2016 from the Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands, after developing a preoperative planning framework for transcatheter aortic valve implantation.
In 2015 he joined the Netherlands Cancer Institute (NKI) as a postdoctoral fellow, where he conducted research for image-guided radiotherapy. In August 2017, he joined Nile University as an assistant professor and Medical Imaging and Image Processing Research Group leader focusing on incorporating deep neural networks in 2-D and 3-D medical image analysis contexts. Mustafa is currently the Artificial Intelligence program director at Nile University. Mustafa’s research interest list includes Medical Imaging (Cardio-Thoracic, Oncology, Digital Pathology, Women Health, Head&Neck), Surgeries and Interventions Planning, Biostatistics, Image Analysis, Computer Vision, Machine Learning, and Knowledge Aggregation for Automatic Machine Learning. He has more than 45 journal articles and conference publications.
Also, he is currently leading the medical imaging and image processing research group focusing on incorporating deep neural networks in 2D and 3D medical image analysis contexts. Mustafa has more than 25 journal articles and conference publications. On the professional side, Mustafa worked in the research and development division at Diagnosoft Inc., 3mensio B.V., PieMedical N.V., and Myocardial Solutions Inc.
- Initiated the first African network for AI and Medical imaging enthusiasts, researchers and scientists.
- IVLP Impact Award from U.S. Department of State (2022).
- Best poster award at the Novel Intelligent and Leading Emerging Sciences Conference (2019).
- Top 5 startups in Young Business Hub Entrepreneurship Investment Summit, Bahrain (2019).
- Fareed Bader Award in World Entrepreneurs and Investments Forum (WEIF) (2019).
- Pitch deck winner and winning the best Health-tech startup at Takeoff Istanbul International Startup Summit after being evaluated by the jury members and 150+ mentors (2019).
- Top 10 Startups to be selected for the “2WiN Mentoring Program” supported by the German Chamber of Commerce (2019).
- Best poster in the Postgraduate Research Forum, Nile University (2018).
- Best Support for research assistant from Nile University (2018).
- Best Support for research assistant from Banque Misr (2017).
- 3rd place in Left ventricular segmentation challenge from cardiac MRI (STACOM 2011).
- Best poster in Image Analysis and Recognition Conference (2010).
- Full scholarship for master’s studies at Nile University (2008).
- Fourth Place in Made in Egypt (MIE) competition for the best graduation project (2007).
License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)
Although the majority of existing License Plate (LP) recognition techniques have significant improvements in accuracy, they are still limited to ideal situations in which training data is correctly annotated with restricted scenarios. Moreover, images or videos are frequently used in monitoring systems that have Low Resolution (LR) quality. In this work, the problem of LP detection in digital
A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes
Machine Learning-based Module for Monitoring LTE/WiFi Coexistence Networks Dynamics
Long-Term Evolution (LTE) technology is expected to shift some of its transmissions into the unlicensed band to overcome the spectrum scarcity problem. Nevertheless, in order to effectively use the unlicensed spectrum, several challenges have to be addressed. The most important of which is how to coexist with the incumbent unlicensed WiFi networks. Incorporating the "intelligence"component into
Light-Weight Localization and Scale-Independent Multi-gate UNET Segmentation of Left and Right Ventricles in MRI Images
Purpose: Heart segmentation in cardiac magnetic resonance images is heavily used during the assessment of left ventricle global function. Automation of the segmentation is crucial to standardize the analysis. This study aims at developing a CNN-based framework to aid the clinical measurements of the left ventricle and right ventricle in cardiac magnetic resonance images. Methods: We propose a
Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-gate UNET
Heart segmentation in Cardiac MRI images is a fundamental step to quantify myocardium global function. In this paper, we introduce a pipeline for heart localization and segmentation that is fast and robust even in the apical slices that have small myocardium. Also, we propose an enhancement to the popular U-Net architecture for segmentation. The proposed method utilizes the aggregation of
Real-time 4-way Intersection Smart Traffic Control System
Since traffic congestion is becoming a regular part of commuters' life, there is a pressing need for better traffic management. Most current traffic control systems are not sensitive to the current state of the roads being controlled, instead they are fixed, timed traffic signals that do not respond to unpredicted congestion. Solutions have been proposed to solve this problem including creating a
Convolutional Neural Network with Attention Modules for Pneumonia Detection
In 2017, pneumonia was the primary diagnosis for 1.3 million visits to the Emergency Department (ED) in the United States. The mortality rate was estimated to be 5%-10% of hospitalized patients, whereas it rises to 30% for severe cases admitted to the Intensive Care Unit (ICU). Among all cases admitted to ED, 30% were misdiagnosed, and they did not suffer from pneumonia, which raises a flag for
Detection of Mammalian Coding Sequences Using a Hybrid Approach of Chaos Game Representation and Machine Learning
Mammalian protein-coding sequence detection provides a wide range of applications in biodiversity research, evolutionary studies, and understanding of genomic features. Representation of genomic sequences in Chaos Game Representation (CGR) helps reveal hidden features in DNA sequences due to its ability to represent sequences in both numerical and graphical levels. Machine learning approaches can
Left ventricle segmentation using scale-independent multi-gate unet in mri images
Left ventricle (LV) segmentation is crucial to assess left ventricle global function. U-Net; a Convolutional Neural Network (CNN); boosted the performance of many biomedical image segmentation tasks. In LV segmentation, U-Net suffered from accurately extracting small objects such as the apical short-axis slices. In this paper, we propose a fully automated left ventricle segmentation method for
- Medical Imaging
- Artificial Intelligence
- Image Analysis
- Knowledge Aggregation
- Graph Optimization
- Clinical Research
- Computational Cardiology