Deep learning technology has been widely applied to multi-source data and imaging in the past decade. It aims to handle multi-modality data from different sources including images, text, audio and sensor data , thereby facilitating seamless integration and interpretation of heterogeneous data for downstream tasks, such as, image classification, object detection, medical imaging analysis, etc. Although the continuous development of deep learning algorithms for multi-source data and imaging has brought significant progress to various fields, there are still challenges in terms of learning efficiency, generalization ability, interpretability and transfer ability.
This Research Topic aims to bring together current research progress (from both academia and industry) on novel deep learning aglorithms to address the challenges to multi-source data and imaging. Specifically, three main objectives are as follows:
• Pursue new discoveries and theoretical foundations in various areas, such as computer vision, data science, biomedical engineering, autonomous driving, etc. For example, which deep learning frameworks can effectively process and fuse multi-source data? How to use deep learning algorithms to improve the accuracy and efficiency of imaging? How to efficiently optimize multi-modal imaging data?
• Develop new deep learning algorithms and tools for multi-source data and imaging. For example, how to design more efficient feature extraction and data fusion methods for multi-source data with complex scenarios? What innovative deep learning models are suitable for specific application scenarios, such as, environmental monitoring and disease diagnosis?
• Explore the application and potential impact of these advanced deep learning algorithms in socioeconomic areas. For example, how can these technologies improve public health and personal health management? How do they contribute to improving the quality and efficiency of medical services? How to overcome ethical challenges while ensuring data privacy and interpretability?
This Research Topic focuses on the theory and applications of deep learning for multi-source data and imaging, aiming to establish a forum for researchers to share their achievements and discoveries. We sincerely invite researchers to submit their original research article to explore advanced deep learning algorithms for multi-source data and imaging. The following topics are the specific interests of this special issue, including, but not limited to:
• Deep learning
• Multimodal analysis
• Zero-shot learning
• Representation learning
• Data fusion
• Reinforcement learning
• Medical image processing
• Transfer Learning
Deep learning technology has been widely applied to multi-source data and imaging in the past decade. It aims to handle multi-modality data from different sources including images, text, audio and sensor data , thereby facilitating seamless integration and interpretation of heterogeneous data for downstream tasks, such as, image classification, object detection, medical imaging analysis, etc. Although the continuous development of deep learning algorithms for multi-source data and imaging has brought significant progress to various fields, there are still challenges in terms of learning efficiency, generalization ability, interpretability and transfer ability.
This Research Topic aims to bring together current research progress (from both academia and industry) on novel deep learning aglorithms to address the challenges to multi-source data and imaging. Specifically, three main objectives are as follows:
• Pursue new discoveries and theoretical foundations in various areas, such as computer vision, data science, biomedical engineering, autonomous driving, etc. For example, which deep learning frameworks can effectively process and fuse multi-source data? How to use deep learning algorithms to improve the accuracy and efficiency of imaging? How to efficiently optimize multi-modal imaging data?
• Develop new deep learning algorithms and tools for multi-source data and imaging. For example, how to design more efficient feature extraction and data fusion methods for multi-source data with complex scenarios? What innovative deep learning models are suitable for specific application scenarios, such as, environmental monitoring and disease diagnosis?
• Explore the application and potential impact of these advanced deep learning algorithms in socioeconomic areas. For example, how can these technologies improve public health and personal health management? How do they contribute to improving the quality and efficiency of medical services? How to overcome ethical challenges while ensuring data privacy and interpretability?
This Research Topic focuses on the theory and applications of deep learning for multi-source data and imaging, aiming to establish a forum for researchers to share their achievements and discoveries. We sincerely invite researchers to submit their original research article to explore advanced deep learning algorithms for multi-source data and imaging. The following topics are the specific interests of this special issue, including, but not limited to:
• Deep learning
• Multimodal analysis
• Zero-shot learning
• Representation learning
• Data fusion
• Reinforcement learning
• Medical image processing
• Transfer Learning