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Call for papers

Scope of the workshop

The domain of medical imaging is experiencing a transformative shift through the application of artificial intelligence and deep learning, providing clinicians with new tools for diagnosis and treatment planning. As these technologies evolve, they hold the potential to significantly enhance the accuracy and efficiency of medical imaging interpretations. Nevertheless, unlike other organs like the brain, lungs or liver, the pancreas has a uniquely complex anatomical structure that sets it apart as a significantly distinct case. Factors such as age, gender and adiposity may largely contribute to variations in pancreas’ size, shape and location. Despite its small size and similarity to surrounding abdominal tissues, diseases affecting the pancreas (diabetes, pancreatic cancer, pancreatitis) pose considerable threats to individuals.

Global statistics reveal a rising mortality rate associated with pancreatic diseases that shows a growing public health emergency worldwide. For instance, as outlined in the annual report of the American Cancer Society, in the United States alone, an estimated 66,440 cases of pancreatic cancer will be diagnosed in 2024, and 51,750 people will die from the disease. These statistics underscore the urgent need for advances in detection, diagnosis, and treatment strategies. AI offers a hope with the potential to significantly enhance diagnostic accuracy and the effectiveness of treatment for pancreatic diseases.

At MICCAI2024, the workshop “AIPAD: AI in Pancreatic Disease Detection and Diagnosis” will focus on the cutting-age of AI applications in pancreatic health, with a special emphasis on image analysis, while also encompassing the broader scope of deep learning applications such as predictive analytics, treatment planning, and outcome evaluation. Our goal is to catalyze collaborative problem-solving and spearhead innovation in this specialized yet critical area of medical imaging. Acknowledging the inherent challenges in pancreatic imaging, such as the low signal-to-noise ratio, prevalent motion artifacts, and the need for precise organ boundary delineation, the workshop will address how AI can navigate these issues to make a meaningful impact. Additionally, we recognize the potential value of a multimodal approach that integrates different data domains, enhancing our collective ability to understand and manage pancreatic diseases more effectively.

Topics

Expected submissions should cover, but are not limited to, the following topics: