Special Session on Deep Learning, Medical Imaging, and Translational Medicine @ IJCNN 2016.

Description: Deep learning has demonstrated its capability for many vision problems, such as face detection and recognition, image classification, etc. It is expected that this technique can benefit the area of medical image analysis, as well as imaging-based translational medicine. Though a few pioneering works can be found in the literature, there are still a lot of unresolved issues when applying deep learning for medical images.

The goal of special session is to present works that focus on the design and use of deep learning in medical image analysis as well as imaging-based translational medical studies. This special session is going to set the trends and identify the challenges of the use of deep learning methods in the field of medical image. Meanwhile, it is expected to increase the connection between software developers, specialist researchers and applied end-users from diverse fields.

Topics of interest to the special session include, but are not limited to:

• Image descriptor and feature extraction;
• Image super-resolution;
• Image reconstruction;
• Image registration;
• Image segmentation and labeling;
• Computer-assisted lesion detection;
• Computer-assisted diagnosis;
• Deep learning model selection;
• Meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures;
• Other related translational medical applications.

Organized by Qian Wang, Jun Shi, Shihui Ying, Manhua Liu and Yonghong Shi
Special Session Web Site