Role of Deep Transfer Learning for Public Health

Deep transfer learning (DTL), a subfield of deep learning, has the potential to revolutionize public health by addressing data limitations and extracting meaningful features. This Research Topic explores its applications, advantages, and challenges in public health, including disease surveillance, medical image analysis, drug discovery, and health behavior analysis. DTL enables effective learning from small datasets by leveraging pre-trained models and extracting relevant features from complex health data. Its generalizability and efficiency make it valuable for resource-constrained settings and time-sensitive interventions.

DTL has wide-ranging applications in public health. It aids in disease surveillance by analyzing diverse data sources, improves diagnostic accuracy in medical image analysis, accelerates drug discovery and repurposing efforts, and analyzes health behavior data for insights. However, challenges such as domain adaptation, model interpretability, bias mitigation, and ethical considerations need to be addressed. Future research should focus on developing tailored transfer learning frameworks for public health tasks and datasets, ensuring fairness, transparency, and accountability. This Research Topic provides a platform to explore the transformative role of DTL in public health and encourages researchers to contribute original work to advance the field.

We invite original research papers, reviews, and case studies that cover a wide range of topics related to the role of DTL in public health. The topics of interest include, but are not limited to:

- DTL for disease prediction and diagnosis
- DTL for infectious disease surveillance and outbreak detection
- DTL in medical imaging for early detection of diseases
- Deep learning models for drug discovery and repurposing
- DTL for personalized medicine and treatment recommendation
- Applications of DTL in health informatics and electronic health records analysis
- Deep learning techniques for analyzing health-related social media data
- DTL in public health interventions and policy-making
- Ethical considerations and challenges of DTL in public health
- Novel methodologies and frameworks for applying DTL in public health research

Keywords: Deep transfer learning (DTL), Public Health, Health Informatics, Disease Surveillance, Medical Image Analysis, Drug Discover, Health Behaviour Analysis

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.