Deep Learning in Biomedical Image Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 70
Special Issue Editors
Interests: machine learning and deep learning methods in healthcare; computer-aided diagnosis; explainable AI; biomedical image analysis; natural language processing
Interests: computer vision; image retrieval; biomedical image analysis; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In this Special Issue, we explore the profound impact deep learning technology has had, and continues to have, on biomedical image analysis. Deep learning algorithms have revolutionized core biomedical imaging applications, demonstrating unparalleled performance in tasks such as image segmentation, registration, reconstruction, and diagnostic imaging. As researchers deepen their understanding of these technologies, they are not only fine-tuning their applications to address increasingly complex healthcare problems but also advancing the underlying algorithms to meet the evolving challenges of modern healthcare.
As these technologies progress, it is essential to integrate the principles of responsible AI, such as transparency and equity, into their development. By doing so, we ensure that the deep learning systems we create are accurate, unbiased, and ultimately aligned with the best interests of patients and healthcare providers alike.
Additionally, deep learning generative models are powerful tools in medical image analysis, enabling the synthesis of images and data augmentation for improved clinical outcomes.
The papers featured in this Special Issue highlight some of the most exciting advancements in the field, presenting cutting-edge research that balances technical innovation with responsible AI practices. We hope this collection of papers will serve as a catalyst for ongoing discussions, fostering a more responsible and effective use of deep learning models in biomedical image analysis.
Dr. Shereen Fouad
Dr. Cecilia Di Ruberto
Guest Editors
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Keywords
- advances in deep learning technology for medical imaging
- deep learning in biomedical image analysis
- deep learning in diagnostic imaging
- deep learning for image segmentation
- deep learning and generative ai in medical imaging
- explainable deep learning methods in medical imaging
- reinforcement learning in medical imaging
- transfer learning in medical imaging
- model optimization in medical imaging
- advanced neural network architectures
- performance evaluation of deep learning methods
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