Applications of Deep Neural Networks in Biomedical Informatics
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 29
Special Issue Editors
Interests: unsupervised learning; transfer learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent systems; machine learning; biological system optimization
Special Issues, Collections and Topics in MDPI journals
Interests: in-process quality control; 3D scene reconstruction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep neural networks (DNNs) have emerged as transformative tools in biomedical informatics, enabling breakthroughs in healthcare through their capacity to analyze complex, high-dimensional data such as medical images, genomics, electronic health records (EHRs), and drug compounds.
Advances in architectures like convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer models have driven innovations in disease diagnosis, drug discovery, and personalized medicine, while techniques such as federated learning and multimodal data fusion address challenges of data privacy and holistic patient modeling. However, persistent hurdles—including data scarcity, model interpretability, ethical concerns, and integration into clinical workflows—underscore the need for interdisciplinary collaboration to translate algorithmic research into real-world impacts.
This Special Issue seeks to bridge these gaps by highlighting cutting-edge methodologies and applications, spanning technical advances (e.g., self-supervised learning and explainable AI), biomedical use cases (e.g., automated medical imaging analysis, clinical NLP, precision oncology), and translational considerations (e.g., regulatory compliance and bias mitigation). By uniting computational innovation with clinical and biological expertise, this issue aims to accelerate the development of robust, equitable, and deployable AI solutions that redefine healthcare delivery and patient outcomes.
Dr. Wenming Cao
Dr. Man-Fai Leung
Dr. Dan Dai
Guest Editors
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Keywords
- medical image analysis
- precision medicine
- explainable AI
- multimodal data fusion
- healthcare data analysis
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