The integration of medical images acquired from distinct modalities represents a burgeoning field at the intersection of medical science and engineering. This convergence has transitioned from theoretical exploration to practical implementation, as evidenced by the increasing presence of advanced medical devices in clinical settings. The ongoing technological advancements and the accumulation of vast medical datasets have underscored the importance of integrating these datasets for comprehensive analysis and clinical diagnosis. However, the inherent characteristics of medical big data—such as multi-modality, high dimensionality, complexity, and uncertainty—pose significant challenges to traditional data processing methods. These challenges hinder the effective extraction and integration of information across diverse data modalities, complicating diagnostic and therapeutic processes for healthcare professionals. Recent studies have demonstrated the potential of multi-modal fusion and analytical technologies to harness heterogeneous data sources within medical big data, thereby improving data processing efficiency and accuracy. Despite these advancements, there remains a critical need for further research to address the complexities of registration, fusion, information extraction, and cross-domain migration in medical data.

This research topic aims to explore and advance multi-modal fusion technologies that integrate various forms of medical data, including imaging omics, radiographic imaging, ultrasonic imaging, genetic sequences, and clinical big data. By employing state-of-the-art methodologies such as computer graphics, machine learning, pattern recognition, and data mining, the research seeks to overcome the challenges associated with the integration of these diverse data sources. The ultimate objective is to develop a comprehensive system that enhances the precision of imaging-driven diagnostics for prevalent diseases, thereby providing essential technological support for the rapid and accurate execution of clinical diagnosis and treatment.

To gather further insights into the integration of medical images from distinct modalities, we welcome articles addressing, but not limited to, the following themes:
• Genomics analysis
• Integration of medical information
• Medical image analysis
• Medical image detection, localization, classification
• Sequence data analysis
• Fusion and feature extraction of cross-modal data
• Cross-domain transfer of multi-center medical data

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Methods and Strategies for Integrating Medical Images Acquired from Distinct Modalities