Special Sessions
Multimodal Frameworks in Healthcare Diagnostics
Noha Ghatwary, Arab Academy for Science and Technology (AASTMT), Neda Azarmehr, University of Sheffield
Multimodal models in machine learning, particularly deep learning, are transforming medical image analysis by incorporating diverse data modalities, such as medical imaging, digital pathology data, lab reports, patient surveys, audio data, health records, and more. By combining data from multiple modalities (e.g., images, texts, and audio), models offer a more holistic understanding of the data. This comprehensive perspective enhances the accuracy and reliability of detection and diagnostic tasks. In addition, these models pave the way for personalised treatment approaches by providing a thorough understanding of patient conditions. In conclusion, multimodal models represent a pivotal step toward achieving a more accurate, robust, and interpreted solution in healthcare and beyond, underscoring their growing importance across research and real-world applications.
Call for Papers Multimodal Frameworks in Healthcare Diagnostics
Visit websiteAI in Surgery
Dr. Hazrat Ali, University of Stirling, Prof. Muhammad Bilal, Birmingham City University, Prof. Shazad Ashraf, University Hospitals Birmingham, NHS Foundation Trust
The special session on AI in surgery aims to explore and identify key areas for rapid developments in AI for surgical care, encompassing its potential, diverse applications, and the practical challenges that lie ahead in integrating AI tools into clinical procedures. The session will bring together domain experts from computer science and clinical surgery to facilitate cross-disciplinary dialogue and share new and existing methods in AI for different surgical procedures. Participants will have the opportunity to exchange ideas and build collaborations that will leave a lasting impact on both research and clinical practice in Surgical AI.
Call for Papers AI in Surgery (AISUR)
Visit websiteFrontiers in Computational Pathology
Dr Adam Shephard, Prof Nasir Rajpoot, Mostafa Jahanifar, Neda Zamanitajeddin (Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK)
Computational pathology is at the forefront of AI-driven medical imaging research, revolutionising histopathological assessment and decision-making. This special session will bring together leading researchers, clinicians, and industry professionals to explore the latest innovations in histology image analysis, multimodal integration, and domain generalisation.
Discussions will focus on cutting-edge developments in deep learning and, foundation models, with an emphasis on translational impact and clinical adoption. By fostering interdisciplinary collaborations, this session aims to push the boundaries of computational pathology and improve patient care.
(TRADI) TRusworthy AI for Computer Assisted Diagnosis and Intervention
Gilberto Ochoa-Ruiz, Techologico de Monterrey, Estefania Talavera, University of Twente, Binod Bhattarai, Univesity of Aberdeen
We welcome original, previously unpublished submissions that align with the scope of the topics mentioned in the MIUA 2025 topics of interests, including:
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Generalization to out-of-distribution samples
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Explainability of machine learning models in healthcare
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Reasoning, intervening, or causal inference
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Debiasing AI models from learning from shortcuts
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Fairness in medical imaging
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Uncertainty estimation of machine learning models and medical data
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Privacy-preserving AI for medical dataLearning informative and discriminative features under weak annotations
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Human-machine cooperation (human-in-the-loop, active learning, etc.) in healthcare, such as medical image analysis
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Multi-modal fusion and learning, such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, pathology, genetics, electronic healthcare records, etc
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Adversarial attack and defence in healthcare
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Benchmarks that quantify the trustworthiness of AI models in medical imaging
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Foundation model pre-training and adaptation
Call for Papers TRrusworthy AI for Computer Assisted Diagnosis and Intervention
Visit websiteSpecial Sessions Submission
To submit to these special sessions please refer to the Abstract and Paper Submission and choose special session on the submission CMT page. You will be able to select one of the Special sessions for which you intend to submit.