Artificial Intelligence and Radiomics in Computer-Aided Diagnosis Call for Papers

CALL FOR PAPERS - AIRCAD 2025 @ICIAP 2025

3rd International Workshop on Artificial Intelligence and Radiomics in 
Computer-Aided Diagnosis AIRCAD 2025

held in conjunction with the 
23rd International Conference on Image Analysis and Processing ICIAP2025, 
Roma, Italy, September 2025

https://sites.google.com/view/aircad2025

AIMS AND SCOPE
In the modern era, healthcare systems predominantly operate with
digital medical data, facilitating a wide array of artificial
intelligence applications. There's a growing interest in
quantitatively analysing clinical images through techniques like
Positron Emission Tomography, Computerised Tomography, and Magnetic
Resonance Imaging, particularly in the realms of texture analysis and
radiomics. Through machine and deep learning advancements, researchers
can glean insights to enhance the discovery of therapeutic tools,
bolster diagnostic decisions, and aid in the rehabilitation
process. However, the huge volume of available data may intensify the
diagnostic effort, exacerbated by high inter/intra-patient
variability, diverse imaging techniques, and the necessity to
incorporate data from multiple sensors and sources, thus giving rise
to the well-documented domain shift issue.

To tackle these challenges, radiologists and pathologists employ
Computer-Aided Diagnosis (CAD) systems, which assist in analysing
biomedical images. These systems mitigate or eradicate difficulties
arising from inter- and intra-observer variability, ensuring
consistent assessments of the same region by the same physician at
various times and across different physicians, thanks to adept
algorithms.

Additionally, significant issues such as delayed or restricted data
access, driven by privacy, security, and intellectual property
concerns, pose considerable hurdles. Consequently, researchers are
increasingly exploring the use of synthetic data, both for model
training and for simulating scenarios not observed in real life.

Furthermore, the emergence of foundation models, such as Vision
Transformers and large multimodal models, represents a paradigm shift
in medical image analysis. These models, pre-trained on vast datasets,
demonstrate remarkable adaptability across various tasks, including
segmentation, classification, and multi-modal integration. Their
ability to generalise effectively offers promising avenues for
addressing domain shift issues and integrating heterogeneous data
sources, enhancing diagnostic and predictive accuracy.

This workshop aims to provide a comprehensive overview of recent
advancements in biomedical image processing, leveraging machine
learning, deep learning, artificial intelligence, and radiomics
features. Emphasis is placed on practical applications, including
potential solutions to address domain shift issues, the utilisation of
synthetic images to augment CAD systems, and the integration of
foundation models into clinical workflows. Ultimately, the aim is to
explore how these techniques can seamlessly integrate into the
conventional medical image processing workflow, encompassing image
acquisition, retrieval, disease detection, prediction, and
classification.


TOPICS
The workshop calls for submissions addressing, but not limited to, the
following topics:
 - Machine and Deep Learning techniques for image analysis (i.e., segmentation of cells, tissues, organs, lesions; classification of cells, diseases, tumours, etc.)
 - Image Registration Techniques
 - Image Preprocessing Techniques (e.g., noise reduction, enhancement of contrast)
 - Image-based 3D reconstruction
 - Computer-Aided Detection and Diagnosis Systems (CADs)  to support clinicians in identifying pathological conditions
 - Radiomics and Artificial intelligence for personalised medicine
 - Machine and Deep Learning as tools to support medical diagnoses and decisions
 - Image retrieval (e.g., context-based retrieval, lesion similarity)
 - Advanced architecture for biomedical image remote processing, elaboration and transmission
 - 3D Vision, Virtual, Augmented and Mixed Reality application for remote surgery
 - Image processing techniques for privacy-preserving AI in medicine.
 - Generation and utilisation of synthetic medical images for model training and validation
 - Foundation models (e.g., Vision Transformers, GPT-like architectures) for medical image analysis and multi-modal data integration
 - Techniques for evaluating the reliability and robustness of synthetic data in clinical scenarios
 - Ethical and Regulatory Aspects in AI-Driven Medical Imaging
 - Frameworks for ethical AI development and deployment in healthcare.
 - Addressing biases and ensuring fairness in AI-driven diagnostic systems.
 - Compliance with regulatory standards for AI-based medical devices
 - Addressing the transparency issue with explainable AI models in clinical practice.

SUBMISSION GUIDELINES 
Accepted papers will be included in the ICIAP 2025 proceedings, which
will be published by Springer as Lecture Notes in Computer Science
series (LNCS). When preparing your contribution, please follow the
guidelines provided on the ICIAP main conference website. The maximum
number of pages is 12 including references. Each contribution will be
reviewed based on originality, significance, clarity, soundness,
relevance and technical content. The submission will be handled
electronically via the Conference's CMT Website:

https://cmt3.research.microsoft.com/AIRCAD2025

Once accepted, the presence of at least one author at the event and
the oral presentation of the paper are expected. For more details
about the registration see the ICIAP main conference details.


IMPORTANT DATES
- Paper Submission : 15 June, 2025
- Notifications to Authors : 30 June 2025
- Camera Ready Papers Due : 10 July, 2025
- Workshop Event: 15/16 September, 2025

ORGANIZERS
Albert Comelli, Ri.MED Foundation, acomelli@fondazionerimed.com
Cecilia Di Ruberto, University of Cagliari, dirubert@unica.it
Andrea Loddo, University of Cagliari, andrea.loddo@unica.it
Lorenzo Putzu, University of Cagliari, lorenzo.putzu@unica.it
Alessandro Stefano, Institute of Molecular Bioimaging and Physiology, National Research Council of Cefalu’, alessandro.stefano@ibfm.cnr.it
Luca Zedda, University of Cagliari, luca.zedda@unica.it

TECHNICAL PROGRAM COMMITEE
Stefano Barone, University of Palermo (Italy)
Viviana Benfante, University of Palermo, Ri.MED Foundation and CNR (Italy)
Monica Bianchini, University of Siena (Italy) 
Roberto Cannella, University of Palermo (Italy)
Renato Cuocolo, University of Naples Federico II (Italy)
Giuseppe Cutaia, University of Palermo (Italy) 
Navdeep Dahiya, Georgia Institute of Technology (USA)
Mario D'Acunto, National Research Council (Italy)
Angelo Genovese,  University of Milano (Italy) 
Marco Grangetto, University of Torino (Italy)
Riccardo Laudicella, University of Messina (Italy)
Salvatore Livatino, University of Hertfordshire (UK)
Carsten Marr, Helmholtz Zentrum München (Germany)
Fausto Milletarì, University of Munich (Germany)
Giovanni Pasini, Sapienza, University of Rome (Italy)
Vincenzo Piuri, University of Milan (Italy)
Giorgio Russo, IBFM-CNR (Italy)
Giuseppe Salvaggio, University of Palermo (Italy)
Franco Scarselli,  University of Siena (Italy)
Alberto Signoroni, University of Brescia (Italy) 
Gaia Spadarella, University of Naples Federico II (Italy)
Arnaldo Stanzione, University of Naples Federico II (Italy)
Lorenzo Ugga, University of Naples Federico II (Italy)
Federica Vernuccio, University of Padova (Italy)
Anthony Yezzi, Georgia Institute of Technology (USA)