معرفی
Anne-Marie Rickmann is a Research Fellow in the Department of Radiology & Biomedical Imaging at Yale University's Yale School of Medicine. Her research focuses on advancing medical imaging techniques through deep learning, with a particular emphasis on image segmentation, cortical surface reconstruction, and domain adaptation in healthcare contexts.
Her work integrates cutting-edge methodologies such as neural deformation fields, diffeomorphic mesh deformations, and foundation models to address challenges in MRI/CT analysis, neuroimaging, and abdominal organ segmentation. She explores applications in pre-clinical cardiology, longitudinal cortex analysis, and post-surgical imaging, emphasizing faithful AI explanations and adaptive learning frameworks.
Notable contributions include the development of tools like AbdomenNet, V2C-Long, and STRUDEL, which enhance precision in medical imaging while addressing issues like hallucination-free segmentation and uncertainty quantification. Her publications reflect a strong focus on interdisciplinary approaches at the intersection of AI, biomedical engineering, and clinical practice.
No scientific awards or grants are explicitly mentioned in the provided materials. She collaborates within the Department of Radiology & Biomedical Imaging, contributing to projects that bridge computational methods with clinical diagnostics and epidemiologic studies.