Machine Learning for Medical Imaging Processing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".
Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 15912
Special Issue Editors
Interests: medical imaging; positron emission tomography (PET); PET/MRI
Special Issue Information
Dear Colleagues,
This Special Issue (SI) encourages authors to present their latest research achievements in relation to new methods and applications of machine learning for medical image processing. Medical image processing involves the use and exploration of 2D or higher dimensional image datasets of the human body obtained from various medical imaging devices to diagnose disease or guide medical interventions. Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Deep neural networks are now state-of-the-art machine learning models used for medical image analysis and processing.
We look forward to the latest research results that suggest feasible solutions for various challenging tasks in medical image processing based on advanced machine learning technology. While not limited to these alone, the typical biomedical image datasets of interest include those acquired from:
X-ray;
Computed tomography;
Magnetic resonance imaging;
Nuclear medicine;
Ultrasound;
Optical and confocal microscopy;
Video and range data images.
Prof. Dr. Jae Sung Lee
Prof. Dr. Quanzheng Li
Guest Editors
Manuscript Submission Information
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Keywords
- Medical image
- Image processing
- Machine learning
- Deep learning
- Classification
- Detection
- Segmentation
- Registration
- Image generation
- Image enhancement