Journal Information
Computerized Medical Imaging and Graphics
http://www.journals.elsevier.com/computerized-medical-imaging-and-graphics/
Impact Factor:
3.298
Publisher:
ELSEVIER
ISSN:
0895-6111
Viewed:
4998
Tracked:
3

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Call For Papers
The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of information concerning the medical use of new developments in imaging diagnosis, intervention, and follow up. Included in the journal will be articles on new medical scanning techniques, image-guided therapy, computer-aided diagnosis, robotic surgery and imaging, augmented-reality medical visualization, imaging genomics, reports of new advances in imaging modalities, and all other information related to the application of computerized radiology, oncology, and surgery. In addition, information on non-medical modes of imaging that have medical applications such as confocal and multi-photon microscopy, optical microendoscope, photoacoustic imaging, infra-red radiation, and other imaging modalities would be welcomed.

The journal is a vehicle for the rapid publication of original research papers and review articles in the field of computerized medical imaging and graphics. Papers published in the journal will be of interest to computer scientists, medical physicists, bioengineers, imaging specialists, medical informaticians, computational biologists, radiologists, oncologists, neurologists, neurosurgeons, surgeons, cardiologists, cardiovascular surgeons, nuclear medicine physicians, pathologists, ophthalmologists, proctologists, nephrologists, gastroenterologists, and internists interested in imaging applications. 
Last updated by Dou Sun in 2019-12-04
Special Issues
Special Issue on Development of new convolutional neural networks architectures in Medical Imaging
Submission Date: 2020-05-31

I. Special issue title: Special Issue on Development of new convolutional neural networks architectures in Medical Imaging II. Aim and Scope: Nowadays, methods of convolutional neural network (CNN) play an important role in medical imaging research, which brings together complementary interdisciplinary research practice, in the development of innovative computer-aided diagnosis (CAD) system, medical imaging reconstruction and segmentation, etc. In the last five years, the successful development of various CNN architectures has significantly improved the performance in medical imaging in terms of diagnostic efficiency, imaging quality, and disease classification accuracy. Recent research indicates that the different architectures of CNN can help achieve an outstanding performance for particular types of medical imaging applications. With the rapidly growing complexities of design issues, methodologies and more demanding quality of medical imaging applications, the development of novel CNN architectures for medical imaging becomes timely and of high relevance. This special issue intends to bring together researchers to report the recent findings in new convolutional neural networks architectures for Medical Imaging. III. Topics of interest This special issue seeks to present and highlight the state-of-the art CNN architectures in medical imaging. Topics include but are not limited to: Computer-aided diagnosis in medical imaging including CT, ultrasound, optical, MRI, and EIT Segmentation in medical imaging Nosie removal in medical imaging Disease classification in medical imaging Medical Image reconstruction
Last updated by Dou Sun in 2019-12-04
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