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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

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AI 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)

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Frontiers in Computational Pathology 

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: 

Call for Papers TRrusworthy AI for Computer Assisted Diagnosis and Intervention

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Special 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.