About this Research Topic
Medical research encompasses a diverse range of data types, including blood test, radiological imaging, histopathological images, molecular data, and clinical records. Integrating and analyzing these varied data sources can significantly enhance our understanding of patients' current conditions, thereby improving the accuracy and reliability of disease screening, diagnosis, and prognosis. Traditional methods, however, often fall short in capturing the complexity and heterogeneity inherent in these data types. The advent of machine learning and deep learning technologies has advanced multimodal imaging and multi-omics integration methods, facilitating the extraction and synthesis of information from disparate sources.
This research topic focuses on the application of machine learning and deep learning in the field of multimodal medical imaging and multi-omics data, and deals with practical problems in clinical practice. It is dedicated to the analysis of diverse data types, including radiological images, histopathological images, radiomics, pathomics, genomics, and clinical records. The main tasks include medical image segmentation, registration, classification and pattern recognition, the development of multimodal diagnostic and prognostic models, and the generation of pseudo medical data. The objective is to advance the application of machine learning and deep learning in multimodal imaging and multi-omics contexts, capture the complexity and heterogeneity of these data types, and identify novel biomarkers for disease diagnosis, treatment, and prognosis.
We welcome contributions from researchers on a range of topics that include, but are not limited to, the following:
1. Combined multimodal data for cancer diagnosis and prognosis analysis;
2. Segmentation and registration of multimodal medical Images;
3. Cross-modal medical image analysis;
4. Clinical predictive modelling analysis;
5. Cancer prognostic analysis based on radiological imaging, histopathological images, and radiogenomics;
6. New methods for multimodal deep learning.
This research topic focuses on the application of machine learning and deep learning in the field of multimodal medical imaging and multi-omics data, and deals with practical problems in clinical practice. It is dedicated to the analysis of diverse data types, including radiological images, histopathological images, radiomics, pathomics, genomics, and clinical records. The main tasks include medical image segmentation, registration, classification and pattern recognition, the development of multimodal diagnostic and prognostic models, and the generation of pseudo medical data. The objective is to advance the application of machine learning and deep learning in multimodal imaging and multi-omics contexts, capture the complexity and heterogeneity of these data types, and identify novel biomarkers for disease diagnosis, treatment, and prognosis.
We welcome contributions from researchers on a range of topics that include, but are not limited to, the following:
1. Combined multimodal data for cancer diagnosis and prognosis analysis;
2. Segmentation and registration of multimodal medical Images;
3. Cross-modal medical image analysis;
4. Clinical predictive modelling analysis;
5. Cancer prognostic analysis based on radiological imaging, histopathological images, and radiogenomics;
6. New methods for multimodal deep learning.
Keywords: Medical image processing and analysis, Healthcare informatics, Deep learning, Machine learning, Clinical decision systems, Multimodal imaging, Multi-omics
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.
