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Dr. Mustafa Elattar, born in Cairo, Egypt in 1986, is a highly accomplished professional in the fields of biomedical engineering, image analysis, medical imaging, and artificial intelligence. He embarked on his academic journey at Cairo University, where he earned his bachelor's degree in Systems and Biomedical Engineering in 2008. He demonstrated his dedication to research and joined the Medical Imaging and Image Processing research group at Nile University, Giza, Egypt, as a research assistant, where he pursued a master’s degree in communication and information technology from Nile University, which he successfully completed in 2010. His research during his master's degree focused on image analysis for cardiac imaging, further honing his expertise in this critical area of medical technology.
Continuing his pursuit of knowledge and innovation, Mustafa received his Ph.D. in Biomedical Engineering and Physics, Faculty of Medicine, in 2016, from the Academic Medical Center, University of Amsterdam, The Netherlands. His doctoral research centered around developing a preoperative planning framework for transcatheter aortic valve implantation, showcasing his proficiency in leveraging advanced technologies to enhance surgical procedures. After completing his Ph.D., Mustafa joined the Netherlands Cancer Institute (NKI) as a postdoctoral fellow in 2016. During his time there, he focused on conducting research for image-guided radiotherapy, further expanding his expertise in the intersection of medical imaging and cancer treatment.
In August 2017, Mustafa joined Nile University as an assistant professor at the Information Technology and Computer Science School. He is also the director of the Artificial Intelligence undergraduate program. Leveraging his extensive knowledge and experience, he established and currently leading the medical imaging and image processing research group, which specializes in incorporating deep neural networks in 2D and 3D medical image analysis contexts. With a strong commitment to sharing his findings and contributing to the scientific community, Mustafa has authored more than 68 journal articles and conference publications, disseminating his research insights and innovations.
Alongside his academic pursuits, Mustafa has gained valuable industry experience. He has worked in the research and development divisions of renowned companies such as Diagnosoft Inc., 3mensio B.V., PieMedical N.V., and Myocardial Solutions Inc. Furthermore, in August 2018, Mustafa founded Intixel Co. S.A.E., where he currently serves as its CEO. Intixel specializes in providing turnkey artificial intelligence solutions tailored to the specific needs of medical imaging solution firms, solidifying Mustafa's reputation as an innovator and industry leader.
Dr. Mustafa Elattar's remarkable academic achievements, extensive research contributions, and entrepreneurial endeavors have positioned him as a prominent figure in the fields of biomedical engineering, medical imaging, and artificial intelligence. His dedication to advancing healthcare through cutting-edge technologies and his commitment to bridging the gap between academia and industry continue to inspire and drive progress in the field.
- 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