[ML-news] [CFP] Pattern Recognition Letters, Special Issue on Synthetic Images to Support Computer-Aided Diagnosis Systems

[Andrea Loddo]

Dear colleagues,

Please find enclosed the call for papers for our Special Issue.

IMPORTANT: The submission period is approaching!

Please accept our apologies for the multiple postings. 

Synthetic Images to Support Computer-Aided Diagnosis Systems – Call for Papers 

https://www.sciencedirect.com/journal/pattern-recognition-letters/about/call-for-papers

 

IMPORTANT DATES

- Submission Period: 1-31 July 2024

- Acceptance Deadline: 9 December 2024

 

DESCRIPTION OF THE ISSUE

Today's health systems collect and deliver most medical data in digital format, mainly thanks to the scientific and technological advances that have led to digitization and increased generation and collection of data describing real-world applications or processes.

 

The availability of medical data enables a large number of artificial intelligence applications, and there is growing interest in quantitative analysis of clinical images, such as Positron Emission Tomography, Computerized Tomography, and Magnetic Resonance Imaging.

 

In addition, machine and deep learning models and data-driven artificial intelligence applications have proven to improve the management and decision-making to improve the discovery of new therapeutic tools, support diagnostic decisions, aid in the rehabilitation process, etc.

 

Despite the potential of data-driven solutions, many problems prevent or delay the development of such solutions. For example, the increasing amount of available data can lead to increased effort to make a diagnosis and is even more challenging due to high inter/intra patient variability, the availability of different imaging techniques, the absence of completely standard acquisition procedures, and the need to consider data from multiple sensors and sources.

 

Additional relevant issues are data access and the representativeness of the captured sample compared to the actual population. Access to real data may be delayed or even prevented for various reasons, such as privacy, security, and intellectual property, or the development of the necessary (quality) acquisition and preparation technology. Sample representativeness is another critical issue involving class imbalance and the representation of rare and extreme events, which is crucial for the performance of artificial intelligence models.

 

For these reasons, researchers have recently explored the use of synthetic data (SD) with three different use cases regarding (i) data augmentation to balance data sets or supplement available data before training a model, (ii) privacy preservation to enable secure and private sharing of sensitive data; and (iii) simulation: to estimate and teach systems in situations that have not been observed in actual reality.

 

The main goals of this special issue are to bring together diverse, new, and impactful research on synthetic data generation for biomedical imaging with a powerful impact on Computer-Aided Diagnosis systems for real-world clinical applications.

 

TOPICS

Topics of interest to this special issue include, but are not limited to:

- Synthetic Images for Privacy-Preserving Computer-Aided Diagnosis Systems

- Computer-Aided Diagnosis Systems Training with Synthetic Images

- Synthetic Images for Benchmarking Computer-Aided Diagnosis Systems

- Medical Image Translation

- Text-guided Medical Image Generation

- Multimodal Medical Image Generation

- Synthetic Images for Computer-Aided Diagnosis Systems Domain Adaptation

- Synthetic Images for Computer-Aided Diagnosis Systems Domain Generalisation

 

SUBMISSION GUIDELINES

The PRL's submission system (Editorial Manager®) will be open for submissions to our Special Issue from July 1st, 2024. When submitting your manuscript, please select the article type VSI: SISCAD. Both the Guide for Authors and the submission portal can be found on the Journal Homepage: Guide for authors - Pattern Recognition Letters - ISSN 0167-8655 | ScienceDirect.com by Elsevier.

  

GUEST EDITORS

Andrea Loddo, University of Cagliari (Italy)

Lorenzo Putzu, University of Cagliari (Italy)

Cecilia Di Ruberto, University of Cagliari (Italy)

Carsten Marr, Institute of AI for Health, Helmholtz Munich (Germany)

Albert Comelli, Ri.MED Foundation (Italy)

Alessandro Stefano, Institute of Molecular Bioimaging and Physiology, National Research Council of Cefalu’ (Italy)