Call for Papers: Special Issue on Sparsity Techniques in Medical Imaging
Best Paper Awards:
Sparsity Based Spectral Embedding: Application to Multi-Atlas Echocardiography Segmentation
Ozan Oktay, Wenzhe Shi, Jose Caballero, Kevin Keraudren, Daniel Rueckert
Predicting cross-task behavioral variables from fMRI data using the $k$-support norm
Michail Misyrlis, Anna B. Konova, Matthew B. Blaschko, Jean Honorio, Nelly Alia-Klein, Rita Z. Goldstein, Dimitris Samaras
Keynote speakers: Yoram Bresler (left) and Daniel Rueckert (right)
Photos of the Workshop
Overview
Sparsity based compressive sensing and sparse learning have been widely investigated and applied in machine learning, computer vision, computer graphics and medical imaging. In the medical community, these methods have been used successfully to speed up MR scan time, MR image reconstruction, organ segmentation from CT and MRI and classification methods for diseases. Sparsity Techniques in Medical Imaging (STMI2014) is the second in a series of workshops on this topic in conjunction with MICCAI 2014. This workshop focuses on major trends and challenges in this area, and aims to identify cutting-edge research work with important potential impact in medical imaging.
Accepted papers will be encouraged to submit to a special issue of The International Journal of Computerized Medical Imaging and Graphics.
September 14th, 2014 8:00 - 12:00 at Boston, MA, USA
Keynote Speakers- Dr. Yoram Bresler from University of Illinois, Urbana-Champaign
- Dr. Daniel Rueckert from Imperial College London
- Tsuhan Chen, Cornell University
- Ruogu Fang, Florida International University
- Zhi-Pei Liang, University of Illinois at Urbana-Champaign
- Dimitris Metaxas, Rutgers University
- Pina C. Sanelli, North Shore - LIJ Health System
- Shaoting Zhang, University of North Carolina at Charlotte
The goal of this workshop is to advance scientific research in sparse methods for medical imaging. It will foster dialogue and debate in this relatively new field which includes Compressive Sensing (CS), Sparse Learning (SL) and their applications to medical imaging. The technical program will consist of previously unpublished and invited papers, with substantial time allocated for discussion.
This workshop will include, but is not limited to the following topics on sparse methods:
Methodology:
- Efficient Sparse Learning
- Dictionary Learning
- Shape Prior Modeling
- Convex Optimization on Sparsity Priors
- Group Sparsity
- Structured Sparsity
- Low-rank Matrix Structures
- Large-scale Sparse Learning
- Multi-Source Sparse Learning
- Statistical Analysis
- Model Selection, etc.
- Image / Signal Reconstruction
- Image Segmentation
- Image Enhancement
- Image Registration
- Compressed Sensing Magnetic Resonance Imaging
- Anomaly Detection and Correction
Cooperating Organization
