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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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DTSTART:20230101T000000
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DTSTART;TZID=Asia/Seoul:20241216T150000
DTEND;TZID=Asia/Seoul:20241216T170000
DTSTAMP:20260430T165619
CREATED:20241022T001632Z
LAST-MODIFIED:20241208T082830Z
UID:10195-1734361200-1734368400@www.ibs.re.kr
SUMMARY:Solving Inverse Problems in Medical Imaging with Score-Based Generative Models - U Jin Choi
DESCRIPTION:In this talk\, we discuss the paper : “Solving Inverse Problems in Medical Imaging with Score-Based Generative Models” by Y Song et al. \nReconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly map measurements to medical images\, leveraging a training dataset of paired images and measurements. These measurements are typically synthesized from images using a fixed physical model of the measurement process\, which hinders the generalization capability of models to unknown measurement processes. To address this issue\, we propose a fully unsupervised technique for inverse problem solving\, leveraging the recently introduced score-based generative models. Specifically\, we first train a score-based generative model on medical images to capture their prior distribution. Given measurements and a physical model of the measurement process at test time\, we introduce a sampling method to reconstruct an image consistent with both the prior and the observed measurements. Our method does not assume a fixed measurement process during training\, and can thus be flexibly adapted to different measurement processes at test time. Empirically\, we observe comparable or better performance to supervised learning techniques in several medical imaging tasks in CT and MRI\, while demonstrating significantly better generalization to unknown measurement processes.
URL:https://www.ibs.re.kr/bimag/event/solving-inverse-problems-in-medical-imaging-with-score-based-generative-models-u-jin-choi/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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