Deep Learning for Biomedical Image Analysis: Recent Advances and Future Trends
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 November 2025 | Viewed by 109
Special Issue Editors
Interests: artificial intelligence; computer vision; specialized computing architectures for real-time image processing
Interests: medical imaging; artificial intelligence; pattern recognition; machine learning; applied physics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning has revolutionized biomedical image analysis, enabling significant advancements in disease diagnosis, treatment planning, and drug discovery. This Special Issue aims to showcase cutting-edge research and novel applications of deep learning techniques in biomedical imaging. We invite original research articles, comprehensive reviews, and insightful perspectives exploring the following topics:
- Novel deep learning architectures: CNNs, RNNs, GANs, transformers, and hybrid models tailored for biomedical image analysis.
- Applications including diverse imaging modalities, such as microscopy, endoscopy, radiology (X-ray, CT, MRI), ultrasound, and multimodal imaging.
- Specific clinical challenges: Cancer detection, neurological disorders, cardiovascular diseases, ophthalmology, and infectious disease diagnosis.
- Methodological advancements: Explainable AI, uncertainty quantification, federated learning, and data augmentation strategies for biomedical images.
- Emerging trends: Integration of deep learning with other technologies, ethical considerations, and the development of robust and clinically translatable solutions.
This Special Issue will provide a platform for researchers to disseminate their latest findings and foster collaborations, ultimately contributing to advancing deep learning for improved healthcare outcomes.
Dr. Madaín Pérez‐Patricio
Prof. Dr. Donato Cascio
Guest Editors
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Keywords
- artificial intelligence
- computer vision
- biomedical imaging
- disease diagnosis
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