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Regular version of the site
Efficient indexing of peptides for database search using Tide

Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.

Journal of Proteome Research. 2023. Vol. 22. No. 2. P. 577-584.

Mint: MDL-based approach for Mining INTeresting Numerical Pattern Sets

Makhalova T., Kuznetsov S., Napoli A.

Data Mining and Knowledge Discovery. 2022. P. 108-145.

Book chapter
Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion

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.

Book chapter
Ontology-Controlled Automated Cumulative Scaffolding for Personalized Adaptive Learning

Dudyrev F., Neznanov A., Anisimova K.

In bk.: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium -23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Springer, 2022. P. 436-439.

Book chapter
Triclustering in Big Data Setting

Egurnov D., Точилкин Д. С., Ignatov D. I.

In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.

Triclusters of Close Values for the Analysis of 3D Data

Egurnov D., Ignatov D. I.

Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.

Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Kudriavtseva P., Kashkinov M., Kertész-Farkas A.

Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708-4717.

Language models for some extensions of the Lambek calculus

Kanovich M., Kuznetsov S., Scedrov A.

Information and Computation. 2022. Vol. 287.

Introduction to Medical Informatics

Course of lectures by Oleg Pianych, PhD, Assistant Professor of Radiology, Harvard Medical School

Programme Author:

Oleg Pianych, PhD, Assistant Professor of Radiology, Harvard Medical School


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.

Course Educational Objectives

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:

  • Acquire knowledge of the basic aims and principles of medical informatics;
  • Study basic methods of medical informatics concepts;
  • Acquire knowledge of basic algorithms in medical informational network construction and functioning;
  • Acquire knowledge of the principles of medical informational network construction and functioning.

Student Requirements

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.

Current Materials

For information about the study materials of the course, please visit the web page.


Basic manuals

Lecture Materials

Additional Manuals

  • Pianykh O.S., ‘Digital Imaging and Communications in Medicine (DICOM): A Practical Introduction and Survival Guide’, Springer, 2008, 383 pages
  • Rangayyan R. M., ‘Biomedical Image Analysis’, CRC Press, 2005, 1272 pages
  • Selected articles of the leading international periodicals (PubMed)

Programme Contents

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.