Journal of Imaging Science and Technology
Call for Paper
Special Issue -Machine Learning in Biomedical Image Processing and Clinical Diagnosis
Submission deadline: Nov. 15, 2020
The rapid development of computer hardware and software technology has laid the foundation to successfully applying advanced artificial intelligence algorithms in massive medical data processing, medical testing, clinical diagnosis, and various branches of medicines. With the assistance of deep learning technologies, researchers and medical professionals have made huge stride in uncoverinig complicated hidden features of diseases and patients from ever-expanding medical data. To a certain extent, Big data-based deep learning algorithms can be regarded as extensions of medical doctor’s knowledge and experience.
Applying machine learning algorithms on medical images has already made significant contribution in clinical practice. Through deep learning methods, human physiology and dysfunctions could be accurately and efficiently characterized by analyzing pathological images using the computer-aided diagnosis system powered by the latest AI architectures and innovations. In the field of medical imaging, diagnostic recommendation can be automatically presented by quantitatively analyzing the characteristics of relevant image data to help radiologists improving the accuracy of diagnosis and the consistency of disease interpretation, which provides objective metric and has proven invaluable in alleviating burdens of subjective judgement by a radiologist in the diagnosis process. Consequently, the improved solutions in machine learning for biomedical imaging will be instrumental in optimizing the diagnosis process and dysfunction prediction/prevention, such as cancer, cardiovascular, cerebrovascular diseases, etc., which will lead to a holistic strategy for disease management and revolutionize the future of the healthcare system. There is no doubt that machine learning in biomedical imaging science will be indispensable in future clinical and basic research for human health.
This Special Issue is dedicated to the novel application of AI in biomedical data process and imaging science. We invite researchers to submit their manuscripts that covers novel AI-based application in biomedical data analysis and Imaging process, Computer-aided diagnosis, disease prediction and prevention system. However, the scope of this Special Issue is not limited to this topic, Machine learning and deep learning for biomedicine, intelligent & virtual diagnosis and treatment system, and other related researches focused on AI in healthcare and disease, are also especially welcome. We encourage the submission of original researches in the fields as well as high-quality reviews with new insights. All submitted papers will be evaluated on relevance, the significance of contribution, technical rigor, and quality of presentation by two to three independent reviewers.
Topics for this special issue include, but are not restricted to, the following fields:
• AI-based Biomedical imaging process
• Biomedical Image Recognition
• Computer-aided diagnosis
• Intelligent Diagnosis and Treatment
• Clinical decision support systems (CDSS)
• Machine learning and deep learning for imaging medicine
• Disease prediction and prevention system
• AI for Healthcare
Submission and Review
You will find submission details in the JIST Author Guidelines. You must include a cover letter with the names of the authors and their affiliations, addresses, faxes, and e‐mails. Prospective authors can submit complete manuscripts electronically via https://jist.msubmit.net by NOV 15, 2020. All submitted papers will be reviewed by at least two reviewers and selected based on their originality, significance, relevance, and clarity of presentation.
• Submission deadline: Nov 15, 2020
• First round author notification: Jan 15, 2021
• First round revision submission: Feb 28, 2021
• Second round author notification: March 21, 2021
• Second round revision submission: April 15, 2021
• Final notification: May 01, 2021
• Publication: TBD in 2021
Prof. Zhiwei Luo, Lead Guest Editor
Institute of Systems Informatics
Prof. Xiaoguang Zhou
School of Automation
Beijing University of Posts and Telecommunications,
Dr. Jie Deng
Rush University Medical Center,
Chicago, IL, USA