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<p class="MsoNormal" style="text-align:justify"><b>Title:</b> Toward Next-Generation Medical Care: Computer Vision for Healthcare AI<o:p></o:p></p>
<p class="MsoNormal" style="text-align:justify"><o:p> </o:p></p>
<p class="MsoNormal" style="text-align:justify"><b>Abstract:</b> In the past two years, transformative AI products such as ChatGPT have underscored the profound impact of AI in our daily lives. Beyond natural language and image understanding, AI is revolutionizing
healthcare, offering advantages such as assisting in diagnosis and treatment, enhancing healthcare safety and reliability, and reducing the financial burden of medical care. As a pivotal technique for processing medical imaging data, medical computer vision
has played a crucial role in developing healthcare AI systems. However, it comes with its own challenges including the complexity of different diseases and the difficulty of collecting large-scale annotated data.
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<p class="MsoNormal" style="text-align:justify">In today’s talk, I will show how we address these challenges in our research work. Firstly, I will introduce our work on robust biomedical image analysis and synthesis, demonstrating the utilization of clinical
prior knowledge for robust medical image analysis and synthetic augmentation with large-scale pretraining to alleviate limited annotation issues in the medical domain. Secondly, I will introduce our research on video analysis and synthesis for AI diagnosis,
where I will illustrate our proposed video-based healthcare AI systems that can process infant movement videos and patient talking videos for the diagnosis of cerebral palsy and stroke. I will also discuss our proposed generative models for video synthesis
and their application to healthcare AI. Finally, I will share insights into future work in this exciting field.<o:p></o:p></p>
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<p class="MsoNormal" style="text-align:justify"><b>Short Bio:</b> Mr. Haomiao Ni is a final-year PhD student in informatics at the College of Information Sciences and Technology, Pennsylvania State University, under the supervision of Prof. Sharon Huang. He
received a master’s degree in computer science from the University of Chinese Academy of Sciences and a bachelor’s degree in information engineering from the South China University of Technology. Mr. Ni’s research interest broadly lies in machine learning
and computer vision, especially with the applications in generative AI, biomedical, and healthcare domains. His research has been published in top-tier conferences and journals in the fields of medical image processing and computer vision,
<span style="mso-fareast-language:ZH-CN">such as </span>MICCAI, CVPR, and Medical Image Analysis.<o:p></o:p></p>
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