Transforming medical imaging with advanced deep learning techniques

This topic explores the latest advancements in deep learning for medical imaging, with a focus on emerging paradigms such as multimodal integration, transformers and vision-language models (VLMs). Medical imaging plays a pivotal role in diagnosis, treatment planning, and disease monitoring, but traditional approaches often face limitations in handling complex, high-dimensional, and heterogeneous data. Recent advances in deep learning have enabled the development of powerful models capable of extracting meaningful patterns from diverse imaging modalities, such as MRI, CT, X-ray, and ultrasound, as well as integrating clinical and textual data. Innovations like transformers and VLMs are pushing the boundaries of automated image interpretation, report generation, and cross-modal analysis. This topic aims to bring together cutting-edge research that enhances the accuracy, efficiency, and interpretability of medical imaging systems, accelerating their clinical translation and impact on patient care.



The goal of this topic is to advance medical imaging through cutting-edge deep learning techniques, addressing challenges such as limited generalization, interpretability, and multimodal data integration. Current models often fall short in leveraging the full potential of diverse medical data, including imaging, clinical notes, and genomic information. Recent breakthrough, such as transformers, vision-language models (VLMs), and multimodal fusion, offer powerful tools for improved diagnostic accuracy and automation.

This topic seeks contributions that showcase novel methods and applications of these technologies, with a focus on clinical relevance, interpretability, and scalability to support next-generation medical imaging and better patient outcomes.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Deep Learning; Medical Imaging; Multimodal Fusion; Transformers; Vision-Language Models (VLMs); Explainable AI (XAI); Causal AI; Clinical Translation

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.