The “Machine Learning for Medical Image Analysis” Research Topic is dedicated to presentations from the 29th Conference in Medical Image Understanding and Analysis 2025 (MIUA 2025), taking place on 15-17th of July 2025 in Leeds. Participants of the conference are welcome to submit full articles to this Frontiers in Medical Technology Research Topic, led by Dr Sharib Ali, Dr Ping Lu, Dr Duygu Sarikaya and Dr Gilberto Ochoa-Ruiz.
The scope of this event focuses on advancing the application of Machine Learning techniques in medical image analysis, a rapidly evolving field at the intersection of artificial intelligence, computer vision, and healthcare. The aim is to bring together researchers, clinicians, and industry experts to share recent developments, novel algorithms, and translational applications that address critical challenges in medical imaging. Topics span a wide range of clinical areas, with a particular focus on improving diagnostic accuracy, enabling early disease detection, enhancing treatment planning, and supporting real-time clinical decision-making. Special attention is given to high-impact domains such as cancer research and cardiac imaging analysis, where machine learning is increasingly used to detect, quantify, and monitor disease progression across diverse imaging modalities. The event also encourages discussion around emerging approaches such as vision-language models, which integrate medical images with textual information to enhance interpretability and enable multi-modal clinical reasoning. By fostering interdisciplinary collaboration, this event seeks to accelerate the development of trustworthy, efficient, and clinically relevant AI solutions that can be deployed in real-world healthcare settings.
Topics of interest include, but are not limited to:
• Supervised, unsupervised, and self-supervised learning for medical image analysis • Deep learning techniques for 2D, 3D, and 4D medical imaging • Multimodal data fusion and learning • Image segmentation, registration, classification, and anomaly detection • Video and image sequence analysis in dynamic medical imaging • Vision-language models for multimodal medical understanding and reporting • Explainable, robust, and trustworthy AI systems in healthcare • Federated, distributed, and privacy-preserving machine learning • Generative models and synthetic data generation for medical imaging • Benchmarking, evaluation metrics, and reproducibility in medical image analysis • Clinical decision support systems driven by AI and imaging data • Applications in radiology, pathology, oncology (especially cancer detection and treatment), cardiac imaging, surgery, and other clinical disciplines
The “Machine Learning for Medical Image Analysis” Research Topic aims to gather contributions from conference participants, particularly focusing on submissions in MIUA 2025.
Given the expected diversity of topics and approaches, all manuscript types accepted by Frontiers in Medical Technology are welcomed, including Original Research, Review, Perspective, and more. Details on article types, article processing charges, and support can be found here and here.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Classification
Clinical Trial
Community Case Study
Curriculum, Instruction, and Pedagogy
Data Report
Editorial
FAIR² Data
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:
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.
