Session chairs

Dr. Ahmad Chaddad, Division of Radiation Oncology, McGill University, Canada
Dr. Jose Dolz, Laboratory for Imagery, Vision and Artificial Intelligence, ETS, Canada

Description

Medical imaging has evolved to become an essential tool for diagnosis, treatment and followup of patient diseases. Nowadays, medical imaging is still on the rise with new image modalities being incorporated and continuous enhancement on image processing techniques. Particularly, over the last years there has been an increasing interest in using machine learning methods to analyse medical data. Some of these techniques have become a breakthrough in the medical image field, demonstrating outstanding performance in a wide range of medical applications. This makes that some of the machine learning subfields, such as deep learning, are steadily growing for solving biomedical and biological imaging and image analysis problems, with increased participation of industry and academia. This special session aims to present advances and original machine learning-based methods of image processing and applications in the field of health. It will be an opportunity to exchange the results of research and new developed techniques in this research field with promising prospects.

The topics of interest include, but are not limited to:

• Medical image classification
• Medical image segmentation
• Deep learning models for medical image analysis
• Data-driven biomarker discovery for disease detection and classification
• Radiomic and radiogenomic analysis

Paper Submission

Please submit your full paper choosing the right track on the Conference Management Toolkit (Microsoft’s CMT) site.  See Paper Submission page for more details. All papers must be written in English and should describe original work. The length of the paper is limited to a maximum of 6 pages (in the standard IEEE conference double column format).

Important dates