Deep Learning for Computer Aided Cancer Detection and Diagnosis with Medical Imaging

Special Issue on Deep Learning for Computer Aided Cancer Detection and Diagnosis with Medical Imaging

Computer aided cancer detection and diagnosis (CAD) has made significant strides in the past 10 years, with the result that many  successful  CAD systems have been developed. However, the accuracy of these  systems  still requires  significant improvement,  so that the can meet the needs of real  world diagnostic  situations.. Recent progress in machine learning offers new prospects   for computer aided  cancer detection and diagnosis. A major recent development is the massive success resulting from the use of  deep learning techniques, which has attracted attention from both  the academic research and commercial application communities. Deep learning is the fastest-growing field in machine learning and is widespread uses in  cancer detection and diagnosis. Recent research has demonstrated that deep learning can increase cancer detection accuracy significantly. Thus, deep learning techniques offer the  promise not only  of  more accurate CAD systems for cancer detection and diagnosis, but may also  revolutionize their design.

This  special issue seeks  high-quality  original research papers  on cancer detection and diagnosis in medical imaging and image processing.  The topics of interest include, but are not limited to:

• Deep learning for cancer tissue classification
• Deep learning for cancer image segmentation
• Deep learning for cancer location
• Deep learning for cancer image retrieval 
• Deep learning for high accuracy computer-aided detection/diagnosis systems
• Deep learning architecture for big cancer data 
• GPU implementation of deep learning techniques for cancer detection/ diagnosis
• Real-time deep learning techniques for cancer detection/diagnosis
• Learning from multiple modalities of imaging data for cancer detection/diagnosis
• Deep learning for big image data analysis and its applications to cancer detection/diagnosis

We are especially welcome the papers describing   new deep learning algorithms and papers which advance pattern recognition methodology.  The selection of the papers will be based on their scientific quality, their contribution to the field of pattern recognition, and their relevance to cancer detection and diagnosis.

All papers will undergo the journal’s usual  review process and will be reviewed by at least three referees. Please refer to the website http://www.journals.elsevier.com/pattern-recognition/  for detailed instructions on paper submission. Papers should be formatted in a single column,  with double spacing and numbered pages, and be between 20 and 35 pages in length.

Submission deadlines

Manuscript submissions due: August 15, 2017

First review completed: Oct. 15, 2017

Revised manuscripts due: Nov. 15, 2017

Second review completed: Dec. 15, 2017

Final manuscript due: Jan. 15, 2018

Targeted issue of Pattern Recognition: March. 2018

Guest Editors

Jinshan Tang, Michigan Technological University, USA. E-mail: jinshant@mtu.edu

Yongyi Yang, Illinois Institute of Technology, USA. E-mail: yangyo@iit.edu

Jianhua Yao, National Institute of Health, USA. E-mail: jyao@cc.nih.gov

SoS Agiagn, University of Texas at San Antonio, USA. E-mail: Sos.Agaian@utsa.edu

Lin Yang, University of Florida, USA. E-mail: lin.yang@bme.ufl.edu