Machine Learning for Biomedical Imaging Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".
Deadline for manuscript submissions: 15 June 2024 | Viewed by 1296
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine learning (ML) has recently become a very popular buzzword as a consequence of disruptive technical advances and impressive experimental results, notably in the field of biomedical imaging. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of ML to clinical applications. With ML becoming a more mainstream tool for typical biomedical image analysis, such as diagnosis, segmentation, or classification, the key to safe and effective use of clinical artificial intelligence (AI) applications depends on both clinicians and AL researchers.
This Special Issue aims to discuss practical applications of machine learning technologies in biomedical imaging and to enable the next generation of strong AI methods, ensuring robust and interpretable AI-based solutions. We hope that clinicians can better understand and effectively use this emerging technology, and AI researchers can further improve models and algorithms from clinical feedback.
To this end, we invite articles that bridge the gap between machine learning research and its biomedical imaging applications to be submitted and published.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Disease detection, disease classification, disease characterization, and disease screening;
- Treatment outcome prediction, treatment response evaluation;
- Image quality improvement, image acquisition acceleration;
- Radiation dose reduction, synthetic image generation across different modalities.
We look forward to receiving your contributions.
Prof. Dr. Jyh-Cheng Chen
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- artificial intelligence
- biomedical imaging
