posted by organizer: arcus || 2403 views || tracked by 5 users: [display]

KALSIMIS 2018 : Knowledge Acquisition and Learning in Semantic Interpretation of Medical Image Structures

FacebookTwitterLinkedInGoogle

Link: http://www.bioimaging.biostec.org/KALSIMIS.aspx
 
When Jan 19, 2018 - Jan 21, 2018
Where Funchal, Madeira
Submission Deadline Nov 7, 2017
Notification Due Nov 21, 2017
Final Version Due Nov 29, 2017
Categories    image analysis   computer vision   machine learning   medical imaging
 

Call For Papers

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.

TOPICS:
Both computer scientists and radiologists are welcome as participants. The session should constitute a perfect forum to express expectations, suggest solutions and share experience for members of those two communities.
The scope of the session contains, but is not limited to, the following topics:
- expert knowledge acquisition and representation methods (how effectively medical knowledge can be acquired and used in existing models of image analysis);
- classical image segmentation and object localization techniques capable of using domain specific knowledge (e.g. active contours and their generalizations);
- structural image representation and analysis (e.g. image decomposition, structured prediction, probabilistic graphical models);
- deep architectures in image analysis (e.g. convolutional neural networks).

Related Resources

ETHE Blearning 2017   Blended learning in higher education: research findings
ICPR 2018   24th International Conference on Pattern Recognition
ECCV 2018   European Conference on Computer Vision
KALSIMIS 2017   Knowledge Acquisition and Learning in Semantic Interpretation of Medical Image Structures
CVPR 2018   Computer Vision and Pattern Recognition
PAKDD 2018   The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining
5th ICTEL 2018   2018 – 5th International Conference on Teaching, Education & Learning (ICTEL)
4th ICTEL 2018   2018 – 4th International Conference on Teaching, Education & Learning (ICTEL)
ICPRAI 2018   International Conference on Pattern Recognition and Artificial Intelligence
ICSC 2018   12th IEEE International Conference on Semantic Computing (ICSC 2018)