Medical Image Analysis: From Small Size Data to Big Data
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".
Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 7077
Special Issue Editors
Interests: AI in medicine; biomedical data mining; object recognition; signal processing; computer-aided food quality inspection; 3D imaging visible and thermal sensors
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical image analysis and biomedical imaging, computer aided cancer detection, biometrics, computer vision, and image understanding
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
A huge amount of medical image data is being created by different medical imaging devices and creating great demand for more effective algorithms to analyze and process them. Extraction of useful information from these data can bring great benefits for medical diagnosis and treatment. However, owing to the limitations of computing power, research on medical image data analysis and processing was mainly focused on small-size or middle-size image data sets in the past. With the development of GPU technology and certain other parallel computing platforms (i.e., Hadoop), the analysis and processing of big medical image data sets is now becoming a new research direction. This Special Issue will focus on recent advances on medical image processing techniques, especially techniques for big medical image data analysis and processing, and we hope that through this publication, we can push the research of image processing in health applications.
Topics of interest include (but are not limited to):
- Deep learning for the analysis of big image data;
- Large learning networks for medical image analysis;
- 3D medical image analysis;
- Image analysis techniques including segmentation, registration, quality enhancement, etc.;
- Image analysis for oncology;
- High-accuracy computer-aided detection and diagnosis systems with medical imaging.
Prof. Sos Agaian
Dr. Jim Tang
Guest Editors
Manuscript Submission Information
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Keywords
- Deep learning for the analysis of big image data
- Large learning networks for medical image analysis
- 3D medical image analysis
- Image analysis techniques including segmentation, registration, quality enhancement, etc.
- Image analysis for oncology
- High-accuracy computer-aided detection and diagnosis systems with medical imaging
