Machine Learning for Medical Image Analysis
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 6347
Special Issue Editors
Interests: medical image analysis; multimodal brain image analysis; image segmentation; computer-aided detection (CAD); computer vision; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: Signal and information processing; machine learning with applications to image, video, communications and security
Interests: medical imaging; artificial intelligence; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With advances in medical imaging, there is an increasing demand for robust algorithms to intelligently and efficiently exploit the breadth of available visual information for better interpretation of medical images. It is rather challenging to derive analytic solutions to represent objects such as organs or lesions due to large variations in intensity, shape, and location of the lesions within areas of complicated anatomies in the images. To this end, machine learning has been successfully employed in many medical image applications including organ/lesion segmentation, tissue classification, computer-aided diagnosis, image-guided therapy, and image registration and fusion. The goal of this Special Issue is to publish the latest research advancements that integrate machine learning for the analysis of medical images. Topics of this Special Issue include (but are not limited to) machine learning methods with their applications in:
• Medical and biomedical image segmentation
• Classification and analysis of anatomical structures, lesions and lesion stages
• Computer-aided detection/diagnosis
• Multiple modality fusion
• Medical image reconstruction
• Pathology image analysis
• Retinal image analysis
Machine learning methods include (but are not limited to):
• Deep neural networks
• Generative adversarial networks
• Artificial neural networks
• Random forest
• Support vector machines
• Bayesian methods
• Manifold learning
Dr. Xujiong Ye
Dr. Victor Sanchez
Dr. Yalin Zheng
Guest Editors
Manuscript Submission Information
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Keywords
- Medical image segmentation
- Computer-aided detection/diagnosis
- Medical/biomedical image analysis
- Multiple modalities
- Artificial Intelligence
- Deep neural networks
- Manifold learning
- Statistical pattern recognition
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