CFP - SI on Medical Image Analysis

 

In recent years, the integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic precision, therapeutic planning, and healthcare delivery. This Special Issue aims to serve as a comprehensive platform for presenting cutting-edge research and fostering collaboration across academia, industry, and clinical practice. We believe that this collection of articles will not only showcase the latest advancements but also inspire future innovations in this dynamic field.

We invite researchers, clinicians, and technologists to contribute original research papers or comprehensive review articles on topics including, but not limited to, the following:

• AI-driven diagnostic tools and predictive models in medical imaging: the development and application of artificial intelligence, including deep learning and machine learning, to enhance diagnostic accuracy and predictive capabilities in clinical practice.・ Radiomics and AI integration: combining radiomics with AI methodologies to extract quantitative imaging features and improve disease characterization, prognosis, and treatment response prediction.
• Automated image segmentation and medical image reconstruction: leveraging AI-driven algorithms for the precise and efficient segmentation of medical images and advanced reconstruction techniques to improve image quality and diagnostic reliability.
• Multi-modal image analysis for enhanced diagnostic accuracy: integrating data from multiple imaging modalities (e.g., CT, MRI, US, PET) using AI to provide comprehensive insights and support clinical decision making.
• Applications of deep learning and machine learning in medical image processing: advancements in neural network architectures and their implementation in tasks such as classification, anomaly detection, and feature extraction.
• Real-world data (RWD) science in AI-driven medical imaging: harnessing large-scale, real-world datasets to train and validate AI models, addressing challenges like data heterogeneity and clinical applicability.
• Ethical considerations, validation, and standardization of AI technologies: ensuring transparency, fairness, and clinical relevance in AI model development and deployment, with a focus on regulatory and ethical compliance.

The scope of this Special Issue reflects the diversity and complexity of AI applications in medical imaging, encompassing advancements in deep learning, machine learning, and AI-driven diagnostics. It addresses critical challenges such as data quality, automated image segmentation, algorithm transparency, and clinical implementation. By highlighting innovations in radiomics and AI integration, medical image reconstruction, and multi-modal image analysis, this issue aims to bridge gaps between research, technology development, and clinical practice.

Through contributions from interdisciplinary fields, we aspire to present a holistic perspective on the current state and future prospects of AI in medical imaging, inspiring transformative solutions for enhanced healthcare delivery.

We look forward to receiving your contributions and collaborating with you to make this Special Issue a landmark in advancing medical imaging and AI innovation.

Dr. Masateru Kawakubo
Prof. Dr. Tamás Haidegger
Guest Editors

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