Multi-modal biomedical data integration solutions play a key role in precision medicine. Advances in genome sequencing, image processing, and medical data management support the collection of multi-modal biomedical data. The integration of multi-modal biomedical data can provide a more thorough look at the impact of a disease on the underlying system. Though multi-modal data processing methods have shown promising performance in many areas, they fail to support the processing of multi-modal biomedical data in clinical practice.
With the new learning algorithms from AI, combining multi-modal biomedical data has been a hot topic. The research topic aims to attract AI technology that facilitates precision medicine by taking multimodal biomedical data into account with efficient algorithms.
For this Research Topic, we invite authors to submit Original Research articles, Brief Research Reports, and Review Articles that provide novel and cutting-edge insights into the field of multi-modal learning and its applications for biomedical data, including but are not limited to:
• Deep learning-based multi-modal learning for medical images;
• Deep learning-based multi-modal learning for omics data;
• Deep learning-based multi-modal learning for risk assessment of biomedical technologies;
• Dimension reduction and visualization of multi-modal data;
• Novel theories and applications of multi-modal biomedical data fusion for accurate clinical diagnoses, such as early diagnosis of Alzheimer’s disease, COVID-19 detection;
• Novel theories and applications of biomedical data augmentation and processing;
• Novel theories and applications of inter-modal association analysis;
• Surveys/review on multi-modal learning and its applications for biomedical data.
Multi-modal biomedical data integration solutions play a key role in precision medicine. Advances in genome sequencing, image processing, and medical data management support the collection of multi-modal biomedical data. The integration of multi-modal biomedical data can provide a more thorough look at the impact of a disease on the underlying system. Though multi-modal data processing methods have shown promising performance in many areas, they fail to support the processing of multi-modal biomedical data in clinical practice.
With the new learning algorithms from AI, combining multi-modal biomedical data has been a hot topic. The research topic aims to attract AI technology that facilitates precision medicine by taking multimodal biomedical data into account with efficient algorithms.
For this Research Topic, we invite authors to submit Original Research articles, Brief Research Reports, and Review Articles that provide novel and cutting-edge insights into the field of multi-modal learning and its applications for biomedical data, including but are not limited to:
• Deep learning-based multi-modal learning for medical images;
• Deep learning-based multi-modal learning for omics data;
• Deep learning-based multi-modal learning for risk assessment of biomedical technologies;
• Dimension reduction and visualization of multi-modal data;
• Novel theories and applications of multi-modal biomedical data fusion for accurate clinical diagnoses, such as early diagnosis of Alzheimer’s disease, COVID-19 detection;
• Novel theories and applications of biomedical data augmentation and processing;
• Novel theories and applications of inter-modal association analysis;
• Surveys/review on multi-modal learning and its applications for biomedical data.