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Computer Methods and Programs in Biomedicine

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영향력 지수:
4.8
출판사:
Elsevier
ISSN:
0169-2607
조회:
24509
팔로우:
3

논문 모집

Computer Methods and Programs in Biomedicine is an academic journal published by Elsevier. (ISSN 0169-2607, impact factor 4.8).

To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software. Computer Methods and Programs in Biomedicine is the companion title to the open access journal Computer Methods and Programs in Biomedicine Update.
최종 수정: Dou Sun ()

Special Issues

Special Issue on Exploring the Frontiers of Radiomics: Unveiling Novel Insights through Advanced Techniques and Multimodal Approaches 투고 마감일: 2027-03-30 Radiomics is a quantitative approach to analyzing medical images in combination with molecular, genetic, and clinical information, which has evidenced very promising results especially in the field of oncology. Radiomics has rapidly evolved into a powerful tool for non-invasive disease diagnosis, prognosis prediction, and treatment response monitoring. This Special Issue aims to gather recent advances and novel contributions from academic researchers and industry practitioners in radiomics research, shedding light on the potential of this burgeoning field to revolutionize personalized medicine. Review or summary articles (e.g., a critical evaluation of the state of the art or insightful analysis of established and upcoming technologies) may be accepted if they demonstrate academic rigor and relevance. Submissions are encouraged to explore various aspects of radiomics, including but not limited to: Secure & Privacy-Preserving AI driving collaborative radiomics model construction: designing and implementing AI algorithms that learn diagnostic predictive models through federated and privacy-preserving methods. This would safeguard sensitive patient data, enabling shared knowledge while upholding individual privacy, revolutionizing non-invasive diagnostics in radiology. EXplainable Artificial Intelligence (XAI) for Radiomics: Utilization of XAI techniques and XAI algorithms for providing interpretable and transparent AI models that elucidate the intricate relationships between radiomic features and clinical outcomes and bridge the gap between complex AI-driven predictions and actionable clinical understanding. Advanced Feature Extraction: Novel algorithms and methodologies for extracting robust and discriminative features from medical images across different modalities, scales, and dimensions. Multimodal Fusion: Investigations into the integration of radiomic features from multiple imaging modalities (e.g., MRI, CT, PET) to enhance diagnostic accuracy and provide a comprehensive understanding of the underlying pathology. Clinical Translation: Studies focusing on the clinical implementation and validation of radiomic models, assessing their real-world utility and impact on patient outcomes. Radiomics in Precision Oncology: Investigations into the application of radiomics in cancer diagnosis, treatment planning, and monitoring, with an emphasis on tailoring therapies to individual patients. Radiogenomics and Radiomics-Pathology Correlation: Research bridging the gap between radiomics features, genomic data, and histopathological findings to uncover hidden relationships and enhance disease characterization. Quantitative Imaging Biomarkers: Development and validation of quantitative radiomic biomarkers for assessing disease progression, treatment response, and prognosis. Open Source Tools and Datasets: Sharing of open-source software tools, libraries, and annotated datasets to foster collaboration and reproducibility in radiomics research. Guest editors: Prof. Dr. Giancarlo G. Fortino University of Calabria, Rende, Italy Dr. Antonella Guzzo University of Calabria, Rende, Italy Professor Filippo Molinari Politecnico di Torino, Turin, Italy Professor Ye Li Shenzhen Institutes for Advanced Technology, Shenzhen, China Prof. Karen Panetta Tufts University, Medford, MA, USA Prof. Maria Francesca Spadea Karlsruhe Institute of Technology, Karlsruhe, Germany Manuscript submission information: You are invited to submit your manuscript at any time before the submission deadline 30 March 2027. Please select “VSI: AI RADIOMICS” as your article type. For any inquiries about the appropriateness of contribution topics, please contact Prof. Dr. Giancarlo G. Fortino via giancarlo.fortino@unical.it Keywords: Radiomics, Artificial Intelligence, XAI, Clinical Translation, Precision Oncology, Multimodal Fusion, Federated Learning, Radiogenomics
최종 수정: Dou Sun ()

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