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Demonstrations provide researchers and practitioners with an exciting and interactive opportunity to present their systems, artifacts and/or research prototypes, either at a regular session or at the technical exhibition. In any case, it is required to avoid a commercial format, even if the demo consists of presenting a business product or service. Instead, the presentation should focus on technical aspects.
Any written support materials may be distributed locally but not published in the proceedings. Authors who already present a paper at the conference may apply for a demonstration, to complement but not to replace their paper presentation. Demonstrations can also be made by sponsor companies or as a mixed initiative involving researchers and industrial partners.
Demonstrations are based on an informal setting that encourages presenters and participants to engage in discussions about the presented work. This is an opportunity for the participants to disseminate practical results of their research and to network with other applied researchers or business partners.

Concerning the format of the demo, we can accommodate it either as a demonstration in a booth (physical area of 4 sq. meter, with a table and 2 chairs) at the exhibition area, as a poster or as a 20 min oral presentation at a session especially set up for demonstrations. It is also possible to organize the presentation of the same demo in more than one format. Please contact the event secretariat.


Systems supporting research for high quality methods of medical image analysis Systems supporting research for high quality methods of medical image analysis (BIOSTEC)
Lecturer(s): Arkadiusz Tomczyk

Systems supporting research for high quality methods of medical image analysis


Arkadiusz Tomczyk
Institute of Information Technology, Lodz University of Technology
Brief Bio
Arkadiusz Tomczyk received the MSc degree in computer science in 2002 and the PhD with honours in computer science in 2011 from the Faculty of Technical Physics, Information Technology and Applied Mathematics of the Lodz University of Technology, Poland. Since 2002 he has been employed in the Institute of Information Technology of the Lodz University of Technology. His research experience covers image processing and analysis, especially active contour methods, as well as pattern recognition and machine learning techniques. From 2007 to 2010 he was engaged as an investigator in research grant devoted to contextual and semantic analysis of medical images and their sequences. From 2013 to 2017 he was a principal investigator in research grant focused on Cognitive Hierarchical Active Partitions, a method combining active contour approach with structural representation of image content. This project was supported by National Science Centre, Republic of Poland, project no. 2012/05/D/ST6/03091. He is an author and co-author of more than 40 journal papers, book chapters and conference contributions.
Current machine learning techniques are able to achieve spectacular results in automatic understanding of natural images, whereas in the area of medical image analysis the progress is not that evident. The problem is medical knowledge essential for proper interpretation of image content. That knowledge, possessed by relatively small number of radiological experts, usually cannot be directly expressed using mathematical formulas. This can be overcome by laborious knowledge acquisition and techniques to some extent imitating expert behavior. Both those tasks, however, are challenging tasks and consequently systems supporting that process become more and more important.

The gathered knowledge can be of use not only to train algorithms automatically analyzing image content but also to objectively evaluate the quality of those algorithms. Such objective analysis is of special importance in case of medical images where results could be used in a diagnostic process. There are some benchmark sets that allow to compare methods proposed by different researchers using the same data. It does not ensure, however, the objective comparison since quality measurement can be different even if data are the same. To overcome this problem systems automatically evaluating algorithms in standardized environments are required.

During the session two systems, PLANTATION and FARM, will be demonstrated. First allows to acquire expert knowledge online without necessity of installing some additional software. It allows not only to annotate image content (including 3D sequences) but also add some additional information both with images and annotations. Second is a web application front-end and computation back-end allowing to evaluate image analysis methods in standardized environment.

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