posted by organizer: arcus || 4802 views || tracked by 7 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 14, 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

17th ICTEL September Barcelona 2020   17th ICTEL 2020 – International Conference on Teaching, Education & Learning, 31 Aug -01 Sep, Barcelona
ICDM 2020   20th IEEE International Conference on Data Mining
DLMIA 2020   Deep Learning on Medical Image Analysis - Journal of Imaging (ESCI)
IEEE-CVIV 2020   2020 2nd International Conference on Advances in Computer Vision, Image and Virtualization (CVIV 2020)
28th ICTEL 19-20 December, Bangkok 2020   28th ICTEL 2020 – International Conference on Teaching, Education & Learning, 19-20 December, Bangkok
MNLP 2020   4th IEEE Conference on Machine Learning and Natural Language Processing
26th ICTEL 16-17 November London 2020   26th ICTEL 2020 – International Conference on Teaching, Education & Learning, 16-17 November, London
ACM--ICMLC--Ei and Scopus 2020   ACM--2020 12th International Conference on Machine Learning and Computing (ICMLC 2020)--SCOPUS, Ei Compendex
27th ICTEL 23-24 November, Kuala Lumpur 2020   27th ICTEL 2020 – International Conference on Teaching, Education & Learning, 23-24 November, Kuala Lumpur
23rd ICTEL 12-13 October, Dubai 2020   23rd ICTEL 2020 – International Conference on Teaching, Education & Learning, 12-13 October, Dubai