Deep learning in radiology - from image analysis to image reconstruction
摘要截稿:
全文截稿: 2019-10-20
影响因子: 3.632
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机:跨学科应用 - 3区
• 小类 : 计算机:理论方法 - 2区
• 小类 : 工程:生物医学 - 3区
• 小类 : 医学:信息 - 3区
Overview
Radiology imaging has become an integral part of disease diagnosis and treatment and is increasingly important. In recent decades, with the rapid development and popularization of medical imaging equipment, medical image data has been expanding. How to efficiently and accurately process these image big data, provide scientific methods and advanced technologies for screening, diagnosis, treatment planning, and efficacy evaluation in clinical medicine, is a major scientific problem that needs to be solved. Image analysis and image reconstruction are the two most important pillars in the field of medical imaging. Deep learning algorithms have demonstrated the potential in the field of medical imaging beyond traditional transform-based or optimization-based methods.
The purpose of this special issue is to demonstrate the new development and application of brain-inspired artificial intelligence algorithms, to solve the problem from image analysis and image interpretation to image reconstruction. The ultimate goal is to promote research and development of deep learning in radiology imaging and other medical data by publishing high-quality research papers in this interdisciplinary field that can profoundly impact the future of the medical industry. Potential topics include, but are not limited to:
Neural network framework for radiological image enhancement and reconstruction.
Deep learning algorithm for fast imaging (MR Image).
Deep neural network in radiological image (X-ray image, etc.) processing and analysis (classification, target detection and others).
Data mining with deep learning in radiological images.
· Personalized diagnosis and personalized treatment based on invasive interventions of deep learning and images.
· Transfer learning and multitasking for radiological image analysis.
· Antagonistic training on radiological images and other medical data.