סמינר: Pixel Club
Generative AI and Foundation Models for Medical Image Synthesis and Analysis
Lecturer:
Yulin Wang
Research Areas:
| High-quality multimodal neuroimaging provides complementary information essential to both neuroscience and neurology. However, due to multifaceted practical limitations, complete imaging data is often unavailable in scenarios such as emergency treatment or routine screening. Generative models offer a promising paradigm to address these bottlenecks by imputing missing modalities, enhancing image quality, harmonizing acquisition domain discrepancies, and simulating diverse disease-relevant appearances. This presentation will explore algorithmic innovations in generative AI, including a variety of model architectures and training strategies, with a focus on their versatile applications across multimodal image generation, cross-field MRI synthesis, image super-resolution, and disease-specific image simulation. Furthermore, we will demonstrate how these synthesized and enhanced images effectively facilitate critical downstream tasks, such as subcortical segmentation, cross-modal registration, and clinical diagnosis, with the ultimate goal of improving patient outcomes. |
| Yulin Wang is a Postdoctoral Researcher at the School of Biomedical Engineering, ShanghaiTech University. In September 2026, she will transition to a postdoctoral position at Cornell Tech, the joint academic venture of Cornell University and the Technion. Her research interests lie at the intersection of medical image computing and analysis, generative artificial intelligence, and neuroradiology, with a primary focus on developing advanced deep learning frameworks to solve challenging reconstruction, synthesis, and enhancement problems in clinical neuroimaging. Her recent works have been published in top-tier journals and conferences, including Cell Reports Medicine, Medical Physics, Physics in Medicine and Biology, and MICCAI.
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