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Pokrovsky boulevard, 11, room S938, Moscow, Russia, 109028
Phone: +7 (495) 772-95-90*27319
The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects.
Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.
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Plos One. 2022. Vol. 17. No. 10.
Zhirayr Hayrapetyan, Nascimento S., Trevor F. et al.
In bk.: Information Systems and Technologies: WorldCIST 2022, Volume 2. Iss. 469. Springer, 2022. P. 141-147.
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In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.
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Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.
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The course is in four parts to cover the whole range of the most interesting issues in medical informatics. The first part (introduction) will provide students with the basic concepts and standards of medical informatics, skills to organize data collection and communication, and practical aspects of doing projects in medical informatics. The second and third parts concern the analysis of medical images, from making and quality optimization, including standardization methods, to automatic computer diagnostics, including pathology identification, and classification.
The forth part will provide students with knowledge of mathematical and applied aspects of medical and informational network functioning, diagnostic data compression, data protection, and the calculation of the optimal parameters of medical complexes.
The course focuses on making students acquainted with medical informatics, a fast developing discipline that combines modern medicine and mathematical methods of medical data analysis. It prepares students for scientific and practical work with modern informational technologies in medicine.
Graduates of the course will:
Students of the course ‘Medical Informatics: Introduction to the Concepts and Analysis of Medical Imaging’ should have a basic knowledge of informatics, mathematical analysis, linear algebra, Matlab programming, and fluent English.
Basic manuals
Lecture Materials
Additional Manuals
Topic 1. What is ‘medical informatics’, and who needs it?
The origin and main objectives of medical informatics. A modern digital clinic. HL7 and DICOM standards. Medical data receiving, storing, and exchange. Basic types and nature of diagnostic images. DICOM’s role in the medical image analysis. The importance of the data quality in diagnostics. Basic functions of medical image preview. PACS, teleradiology. Expert networks.
Topic 2. Basic types of medical imaging mathematical analysis
Computer tomography, and Radon transformation. Basic artifact origin (noise, motion, metal, lines, field irregularity, small doses). Image quality improvement as a standardization task. Bilateral filtration equations. Registration. Segmentation and texture analysis. Active snakes. Laplacyan pyramid.
Topic 3. ‘The patient is more alive than dead’
Diagnostics in time and space. Computer-Aided Diagnosis, and its mathematical methods. Examples of CAD algorithms, and examples of their diagnostic parameters. The role of the decreasing dimension algorithms. Basic requirements to mathematical algorithms in medicine. Medical modeling.
Topic 4. Unusual applications: From Hacking to Art.
Data compression, both ordinary and diagnostical. Medical data hacking and protection. Digital watermarks and steganography in medicine. Individual feature protection. Medical informatics in crime detection: from mummies to weapons. X-ray art and music.