I'm interested in combination of machine learning and image processing methods, and their application in medical image analysis.
I have experience of working with Convolutional Neural Networks, Active Shape Models, and Level-set methods.
Research Interests
Education
| Master's | Allameh Tabataba'i University (Tehran)Computer Science / Intelligent Systems, GPA: 3.7 | Sep 2015 – Sep 2017 |
| Bachelor's | University of Applied Science and Technology (Tehran)Computer / Software Engineering, GPA: 3.4 | Jan 2012 – Feb 2014 |
| Associate's | Shahid Shamsipour Technical College (Tehran)Computer / Software Engineering | Jan 2009 – Sep 2011 |
Master's Thesis
Combined Deep-Learning and Level-set Approach to Segmentation of the Left Ventricle in 3D Cardiac MRI
Supervisor: Farzad Eskandari
Advisor: Mohammadreza Asghari Oskoei
Examiner: Seyed Ali Katanforoush (Shahid Beheshti University, Tehran)
Publications
Designing Evidence Based Risk Assessment System For Cancer Screening As An Applicable Approach For The Estimating Of Treatment Roadmap
Elham Maserat, Reza Safdari, Hamid Asadzadeh Aghdaei, Alireza Khoshsirat, Mohammad Reza Zali
BMJ Open, The 5th International Society for Evidence-Based Healthcare Congress, Kish Island, Iran
February 2017 - Volume 7 - Issue 1
doi: 10.1136/bmjopen-2016-015415.43
http://bmjopen.bmj.com/content/7/Suppl_1/bmjopen-2016-015415.43
Standardized Tests
TOEFL
Total: 101, Reading: 29, Listening: 26, Speaking: 22, Writing: 24
GRE
Quantitative Reasoning: 158 (69th percentile), Verbal Reasoning: 146 (31th percentile), Analytical Writing: 3 (18th percentile)
Professional Service
| Software Programmer | Mehrsys (Tehran)Working on different software projects using up-to-date frameworks and technologies including Java (Spring, Hibernate, JasperReports), NodeJS, AngularJS, Ionic, MongoDB, SQL Server, TypeScript, Wordpress. | Sep 2014 – Present |
| Software Programmer | Raydana (Tehran)Working on an ERP system (Enterprise Resource Planning) using Java (Struts, Spring, Hibernate, JSP, JSF, GWT), Oracle, MySQL, and etc. | Jun 2011 – Sep 2014 |
| Software Programmer | Faranam (Tehran)Frontend and backend software development with ASP.NET MVC, WPF, Entity Framework, SQL Server and jQuery. | Dec 2010 – Jun 2011 |
Technical Skills
Machine Learning | Convolutional Neural Networks, AutoEncoders, Principal Component Analysis |
Image Processing | TensorFlow, Caffe, OpenCV, Active Shape Models, Active Appearance Models, Level-set methods |
Database | Oracle, MongoDB, SQL Server, Sqlite, LevelDB, HDF5 |
Programming Languages | Java, Python, Matlab, NodeJS, C#, TypeScript, HTML, JavaScript, CSS |
Programming Frameworks | JavaEE, Spring, Hibernate, Struts, express.js, mongoose.js, Microsoft .NET, Entity Framework |
User Interface | AngularJS, Telerik, KendoUI, Bootstrap, Ionic, Apache tiles, JavaFX, ASP.NET, ASP.NET MVC, WPF |
Others | Wordpress, Apache Maven, Apache Tomcat, JasperReports, Version Control Systems, gulp.js, CentOS, Photoshop |
Teaching assistantship
Image Processing | Allameh Tabataba'i University Professor Mohammadreza Asghari Oskoei | Spring 2017 |
Logic Programming | Allameh Tabataba'i University Professor Hossein Teymoori | Fall 2016 |
References
Mohammadreza Asghari Oskoei | Allameh Tabataba'i University Professor of Computer Vision | oskoei@atu.ac.ir |
Hossein Teymoori Faal | Allameh Tabataba'i University Professor of Mathematics and Logic | hossein.teimoori@atu.ac.ir |
Mohammad Zebarjad | Mehrsys Co. Chief Executive Officer | info@mehrsys.com |
Appendix
Master's Thesis Abstract
Segmentation of the left ventricle (LV) in cardiac magnetic resonance images (MRI) is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this thesis, we first explain essential concepts, then review existing methods for segmentation of LV. We continue by implementing and evaluating a method which employs deep learning algorithms combined with a level-set method to fully automatically segment the LV in short-axis cardiac MRI datasets.The method employs deep learning algorithms to learn the segmentation task from the ground truth data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV. The inferred shape is incorporated into a level-set method to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MRI datasets taken from the MICCAI 2009 LV segmentation challenge and compared the results to the state-of-the-art methods. Excellent agreement with the ground truth was achieved. We computed validation metrics such as the percentage of good contours, Dice metric, average perpendicular distance, and conformity as respectively 83%, 80%, 3.4mm and 70%.
Keywords: Deep-Learning, Level-set method, Left Ventricle, Cardiac MRI, Machine Learning