🎉 CALL FOR PAPERS - AIRCAD 2025 @ICIAP 2025 🎉

🧠 3rd International Workshop on Artificial Intelligence and Radiomics in Computer-Aided Diagnosis (AIRCAD 2025)
📅 Held with the 23rd International Conference on Image Analysis and Processing (ICIAP 2025)
📍 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 & Deep Learning for image segmentation/classification (cells, tissues, lesions, diseases)
• 📐 Image Registration Techniques
• 🎨 Image Preprocessing (noise reduction, contrast enhancement)
• 🏗️ 3D Reconstruction
• 💡 Computer-Aided Detection & Diagnosis systems (CAD)
• 🧬 Radiomics & AI in personalized medicine
• 🔎 Content-based Image Retrieval
• 🌐 Remote biomedical image processing & transmission architectures
• 🥽 3D Vision, VR/AR/MR for remote surgery
• 🔒 Privacy-preserving AI in medicine
• 🧪 Synthetic medical images for model validation
• 🏥 Foundation models (Vision Transformers, GPT-based) for analysis & multi-modal data integration
• 🛡️ Reliability and robustness of synthetic data
• ⚖️ Ethical & Regulatory Issues in AI medical imaging
• 📋 Frameworks for ethical AI & compliance with standards
• 🧑‍⚖️ Addressing bias, fairness, and transparency with explainable AI

📝 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

🎤 ABSTRACT OF THE TALK (Dr. Carsten Marr)

Diagnosing hematologic malignancies still relies heavily on the subjective visual assessment of cytological and histological images. Experts are increasingly challenged by large volumes of data, the rarity of diagnostic cell types, and the heterogeneous presentation of disease. Despite the availability of comprehensive patient data, advanced deep learning algorithms, and a solid understanding of hematopoiesis, there is currently no robust model capable of automatically analyzing and predicting disease dynamics from blood smears or bone marrow aspirates. In this talk, I will present recent advances in AI-based hematopathology that aim to address key challenges such as model robustness, generalization to real-world data, bias mitigation, and the integration of multimodal sources. I will highlight three promising directions: (1) efficient single-cell detection using neural cellular automata, (2) interpretable feature learning via sparse autoencoders, and (3) the integration of biomedical prior knowledge into model training through customized loss functions. These developments illustrate how tailored AI solutions can bridge the gap between machine learning algorithms and clinical decision-making, paving the way toward more accurate, scalable, and explainable diagnostics in hematology 🩸.

👥 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, IBSBC - CNR of Cefalù, alessandro.stefano@ibfm.cnr.it
Luca Zedda, University of Cagliari, luca.zedda@unica.it