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MIVADL 2018 : Special Track on Medical Image and Video Analysis Using Deep Learning (at IEEE CBMS 2018)


When Jun 18, 2018 - Jun 21, 2018
Where Karlstad, Sweden
Abstract Registration Due Feb 26, 2018
Submission Deadline Mar 5, 2018
Notification Due Apr 25, 2018
Final Version Due May 7, 2018
Categories    medical imaging   deep learning   video analysis   image analysis

Call For Papers

The development of intelligent medical image and video analysis systems has experienced a significant boost in recent years thanks to the growing popularity of deep learning. Many commercial applications using deep learning to analyze, classify, segment, measure, and recognize contents from different modalities of medical images are currently available. This special track provides a forum for the discussion of the impact of deep learning on medical image analysis and a focused venue for sharing novel scientific contributions in the area of deep learning uses in medical imaging.

Topic of interest include (but are not limited to):
- Novel approaches for medical image/exam classification, object/lesion classification, organ/region/landmark localization, object/lesion detection, organ/substructure segmentation, lesion segmentation, and medical image registration using deep learning;
- Content-Based Image Retrieval (CBIR) of medical images using deep learning;
- Medical image content understanding using deep learning;
- Medical image generation and enhancement methods using deep learning;
- Multimodal (image/text) analysis using deep learning;
- Organ-specific, modality-specific, and disease-specific image analysis using deep learning;
- Applications of deep learning for digital pathology and microscopy.

Authors are invited to submit their original contributions before the deadline following the conference submission guidelines.

Paper submission guidelines
Please follow the general conference paper submission guidelines that can be found here:

Special Track Chairs
- Oge Marques, Florida Atlantic University, USA (
- Michael Riegler, Center for Digitalization and Engineering, Norway (
- Klaus Schoeffmann, Klagenfurt University, Austria (

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