Special sessions are very small and specialized events to be held during the conference as a set of oral and poster presentations that are highly specialized in some particular theme or consisting of the works of some particular international project. The goal of special sessions (minimum 4 papers; maximum 9) is to provide a focused discussion on innovative topics. All accepted papers will be published in a special section of the conference proceedings book, under an ISBN reference, and on CD-ROM support. All papers presented at the conference venue will be available at the SCITEPRESS Digital Library. SCITEPRESS is a member of CrossRef and every paper is given a DOI (Digital Object Identifier). The proceedings are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP, EI (Elsevier Engineering Village Index) and Scopus.
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SPECIAL SESSIONS LIST
KALSIMIS 2018, Special Session on Knowledge Acquisition and Learning in Semantic Interpretation of Medical Image Structures
Chair(s): Piotr Szczepaniak, Piotr Grzelak and Arkadiusz Tomczyk
Special Session on Knowledge Acquisition and Learning in Semantic Interpretation of Medical Image Structures -
KALSIMIS
2018
Paper Submission:
November 7, 2017
Authors Notification:
November 21, 2017
Camera Ready and Registration:
November 29, 2017
Scope
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 or by techniques to some extent imitating expert behaviour. Both approaches are, however, still challenging tasks. That is why the goal of the special session is to discuss the problems in acquisition and utilization of domain knowledge in automatic understanding of semantic image structure.