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--- Call for papers - Apologies for multiple posting --- </div>
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<b>Workshop on AI in Pancreatic Disease Detection and Diagnosis</b> </div>
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<i>Held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)” - October 10, 2024, Marrakesh (Morocco) </i> </div>
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<span style="color: rgb(0, 0, 0);"><i>Web site: </i></span><span style="color: rgb(70, 120, 134);"><i><a href="https://aipad-miccai.github.io/" id="OWA1b0435bd-8d8b-89d3-5fcd-745c8ed78b9a" class="x_x_x_Hyperlink x_x_x_SCXW123209729 x_x_x_BCX0 x_x_x_OWAAutoLink" data-auth="NotApplicable" style="color: rgb(70, 120, 134); text-decoration: none; margin: 0px;">https://aipad-miccai.github.io/</a></i></span><span style="color: rgb(0, 0, 0);"> </span></div>
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--------------------DEADLINE EXTENSION-------------------- </div>
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<b>Important dates</b> </div>
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<span style="color: rgb(0, 0, 0);">Paper submission deadline:<s> </s><u><s>June 24, 2024
</s></u></span><span style="color: rgb(237, 92, 87);"><b><u>June 29, 2024</u></b> </span></div>
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Notification to authors: <u>July 15, 2024</u> </div>
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Camera-ready deadline: <u>July 30, 2024</u> </div>
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<b>Invited Speaker</b> </div>
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<b>Dr. Zongwei Zhou, </b>Assistant Research Scientist at Johns Hopkins University </div>
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“Analysis of Pancreatic Tumors by Synthesis” </div>
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<b>About</b> </div>
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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. </div>
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The workshop “AIPAD: AI in Pancreatic Disease Detection and Diagnosis” aims to 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. </div>
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<b>Topics</b> </div>
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Potential topics include, but are not limited to: </div>
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AI-driven techniques for enhancing pancreatic image quality and detail resolution. </div>
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Deep learning models for accurate classification and segmentation of pancreatic structures. </div>
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Multi-modal data integration using AI to provide a comprehensive view of pancreatic health. </div>
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Addressing small sample sizes and class imbalance in pancreatic datasets with AI. </div>
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AI solutions for artifact reduction and signal enhancement in pancreatic imaging. </div>
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Utilizing transfer learning to supplement pancreatic imaging data scarcity with larger, external datasets. </div>
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Weakly supervised learning for the detection, characterization, and risk assessment of pancreatic lesions with minimal annotated data. </div>
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Use of foundational models in the development of diagnostic, prognostic, and therapeutic tools for pancreatic diseases. </div>
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Examination of privacy-preserving techniques in AI for pancreatic health, addressing the challenges of data protection and confidentiality while enabling collaborative research and data sharing. </div>
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Development of interpretable AI models for pancreatic imaging and data analysis, emphasizing the need for clear decision-making due to the pancreas’s complex anatomy and the often subtle presentation of pancreatic diseases. </div>
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Creation and curation of richly annotated pancreatic datasets for AI applications. </div>
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AI methodologies for robust segmentation and quantification of pancreatic tumors. </div>
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Tailored evaluation and validation frameworks for AI tools in pancreatic imaging. </div>
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Discussion of ethical implications surrounding the application of AI in pancreatic imaging. </div>
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<b>Proceedings</b> </div>
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Accepted papers will be published in Springer LNCS in a separate proceedings book. </div>
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<b>Organizers</b> </div>
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Federica Proietto Salanitri - University of Catania, Italy </div>
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Serestina Viriri - University of KwaZulu-Natal, South Africa </div>
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Ulas Bagci - Northwestern University, USA </div>
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Pallavi Tiwari - University of Wisconsin Madison, USA </div>
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Boqing Gong - Google DeepMind, USA </div>
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Concetto Spampinato - University of Catania, Italy </div>
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Zheyuan Zhang - Northwestern University, USA </div>
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We hope to see you in Marrakesh! </div>
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