Aims & Scope

The International Journal on Imaging and Image-Computing in ALL Medical Specialties. The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging and/or affiliated biomedical data in disease detection, diagnosis, intervention, prevention and monitoring, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging and/or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, biomedical data integration and visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, image-omics, and imaging integration and modeling with other information relevant to digital health such as video, audio, and genomic data. Medical imaging modalities include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, endoscopy, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets to address disease and health problems.

The journal is a vehicle for the rapid publication of original research papers and review articles in the multi-disciplinary field of computerized medical imaging and graphics. Papers published in the journal will be of interest to imaging specialists, computer scientists, electrical, mechanical, and biomedical engineers, medical physicists, clinical informaticians, radiologists, pathologists, surgeons, and other physicians and internists interested in novel imaging technologies and applications in medicine.

Special Issue on Trustworthy Artificial Intelligence for Medical Imaging
截稿日期: 2024-12-31

Artificial intelligence (AI) has achieved or even exceeded human performance in many medical imaging tasks, owing to the fast development of AI techniques and the growing scale of medical data. However, AI techniques are still far from being widely applied in medical imaging practice. Real-world scenarios are far more complex, and AI is often faced with challenges in its credibility such as lack of explainability, generalization, fairness, privacy, etc. The development of trustworthy artificial intelligence for medical imaging is hence of great importance to enhance the trust and confidence of doctors and patients in using the related techniques. 

Guest editors:

Prof. Hao Chen
The Hong Kong University of Science and Technology, Hong Kong, Hong Kong(Trustworthy AI, medical image analysis, deep learning, computer vision, bioinformatics)

Prof. Yuyin Zhou
University of California, Santa Cruz, California, United States of America(AI for healthcare, medical image computing, computer vision, machine learning)

Dr. Luyang Luo
Harvard University, Boston, United States of America(Trustworthy AI, label-efficient learning, multimodal learning, biomedical data analysis, foundation model)

Prof. Lequan Yu
University of Hong Kong, Hong Kong, Hong Kong(Machine learning, biomedical data analysis, multimodal learning, real-world learning, causality-driven learning)

Dr. Junlin Hou
The Hong Kong University of Science and Technology(Deep learning, medical image analysis, explainable AI, label-efficient learning)  

Dr. Xi Wang
The Chinese University of Hong Kong, Hong Kong, Hong Kong(Weakly supervised learning, semi-supervised learning, medical image analysis, AI for drug discovery, precision oncology )

Special issue information:

This special issue aims to address the critical need for developing trustworthy AI algorithms to accelerate the integration of AI in medical imaging. We welcome original research articles and comprehensive reviews that contribute to the advancement of trustworthy AI in medical imaging.

Topics of interest will include, but not be limited to:

    Generalization to out-of-distribution samples.
    Explainability of AI models in medical imaging.
    Reasoning, intervening, or causal inference.
    Debiasing AI models from learning from shortcuts.
    Fairness in medical imaging.
    Uncertainty estimation of AI models and medical images.
    Privacy-preserving AI for medical imaging.
    Learning informative and discriminative features under weak annotations.
    Human-AI cooperation (human-in-the-loop, active learning, etc.) medical imaging.
    Multi-modal fusion and learning, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, pathology, etc.
    Adversarial attack and defense in medical imaging.
    Benchmarks that quantify the trustworthiness of AI models in medical imaging tasks.

Manuscript submission information:

Manuscript submission deadline: 31/12/2024

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Prof. Hao Chen via jhc@cse.ust.hk.

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: TAI4MI” when submitting your manuscript online at the journal’s submission platform Editorial Manager® . Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

Keywords:

(Generalization) AND (Explainability) AND (Reasoning) AND (Debias) AND (Fairness) AND (Uncertainty) AND (Privacy-preserving AI) AND (Human-AI cooperation) AND (Multi-modal) AND (Foundation model)

Special Issue on Computerized Approaches for the Development of AI-based Clinical Decision Support Systems Integrated into Clinical Practice
截稿日期: 2025-05-31

In the ever-evolving landscape of healthcare, the integration of cutting-edge technologies has become imperative for enhancing clinical decision-making processes.

Clinical Decision Support Systems (CDSS), incorporating Artificial Intelligence (AI) models, have emerged as powerful tools for increasing healthcare professionals' abilities to provide precise and timely patient care. Therefore, this Special Issue focuses on the latest advancements in AI-powered CDSS implementation, emphasizing their clinical validation and integration into the clinical practice. The Special Issue encourages methodological contributions, but also applicative papers are welcome.

Guest editors:

Dr. Leonardo Rundo
Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, Italy

Dr. Francesco Prinzi
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy

Dr. Michail Mamalakis
Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

Dr. Carmelo Militello
Institute for High Performance Computing and Networking National Research Council Palermo Branch, Palermo, Italy

Special issue information:

In the ever-evolving landscape of healthcare, the integration of cutting-edge technologies has become imperative for enhancing clinical decision-making processes [1]. Clinical Decision Support Systems (CDSS), incorporating Artificial Intelligence (AI) models, have emerged as powerful tools for increasing healthcare professionals' abilities to provide precise and timely patient care [2,3]. Therefore, this Special Issue focuses on the latest advancements in AI-powered CDSSs implementation, emphasizing their validation and integration into the clinical practice [4].

The Special Issue encourages methodological contributions, but also applicative papers are welcome. Below are outlined some key thematic areas that this Special Issue aims to explore:

    Integration of CDSS into the clinical practice: advancing the CDSS frontier via rigorous implementation strategies and robust clinical validation of machine learning-based models. Model deployment and use must demonstrate improved patient care and outcomes.
    Feature Extraction: pioneering standardized techniques for extracting informative features from medical data, including but not limited to radiomics, while exploring innovative approaches for embedding extraction from complex, high-dimensional datasets – such as medical images, time series, and multi-omics data – facilitating deeper insights and informed clinical decision-making.
    Multimodal Data integration: novel methods and architectures for integrating heterogeneous multimodal data sources, aiming to harness the collective intelligence embedded within different data types to unveil comprehensive insights and enhance diagnostic accuracy.
    Explainable AI in medicine: development of innovative post-hoc explainability methods, emphasizing key explanatory features to enhance the interpretability of CDSS. Exploring novel perspectives regarding the role of Explainable AI and its applications in medicine can be also considered.
    Training in small-dataset scenarios: innovative strategies and methodologies for effective model training and validation when confronted with limited data availability, encompassing techniques such as transfer learning, data augmentation, and synthetic data generation to optimize model performance and generalization capabilities in clinical settings.

To ensure both workflow reproducibility and generalization capabilities, the proposed models should be trained on public datasets and validated on external datasets (proprietary and/or public).

Submissions are encouraged to explore various aspects of clinical scenarios, including but not limited to diagnosis, prognosis, response to treatment, primary prevention, data retrieval, etc.

Manuscript submission information:

Manuscript submission open date: 01/08/2024
Manuscript submission deadline: 31/05/2025

You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Dr. Francesco Prinzi (francesco.prinzi@unipa.it) or Dr. Carmelo Militello (carmelo.militello@cnr.it).

Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: AI-based CDSS” when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.

References:

    Tomassini, S., Falcionelli, N., Bruschi, G., Sbrollini, A., Marini, N., Sernani, P., ... & Burattini, L. (2023). On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Computerized Medical Imaging and Graphics, 110, 102310.​
     Ahamed, M. F., Hossain, M. M., Nahiduzzaman, M., Islam, M. R., Islam, M. R., Ahsan, M., & Haider, J. (2023). A review on brain tumor segmentation based on deep learning methods with federated learning techniques. Computerized Medical Imaging and Graphics, 102313.
    Ahmedt-Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2022). A survey on graph-based deep learning for computational histopathology. Computerized Medical Imaging and Graphics, 95, 102027.
    Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17.

Keywords:

medical image analysis, feature extraction, multimodal data integration, explainable AI