Advanced Medical Imaging: Techniques for Accurate Results with Limited Annotation Effort
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 130
Special Issue Editors
Interests: machine learning; advanced machine learning; neural networks and artificial intelligence; computer vision; applied artificial intelligence; prediction
Interests: machine learning; advanced machine learning; neural networks and artificial intelligence; computer vision; applied artificial intelligence; prediction
Special Issue Information
Dear Colleagues,
This Special Issue, titled "Advanced Medical Imaging: Techniques for Accurate Results with Limited Annotation Effort", seeks to explore the integration of AI in enhancing diagnostic accuracy in medical imaging with minimal manual annotations. We invite original research and review articles that demonstrate innovative AI methodologies, such as transformer architectures, training with existing medical reports, and self-supervised learning.
Submissions should focus on AI-driven techniques that integrate seamlessly into clinical workflows across various medical imaging domains, including radiology, pathology, gastroenterology, etc. We are particularly interested in research that addresses model scalability, implementation challenges, and clinical application.
We aim to feature high-quality research that advances both technology and clinical practices, making sophisticated diagnostics more accessible and efficient. Contributions that enhance transparency and understanding of AI systems in medical settings are highly encouraged.
We look forward to receiving insightful contributions.
Dr. Adrian Krenzer
Prof. Dr. Frank Puppe
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- deep learning
- transformers
- self-supervision
- diagnostics
- radiology
- pathology
- semi-supervised learning
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