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检索条件"机构=Institute for Machine Learning in Biomedical Imaging"
98 条 记 录,以下是41-50 订阅
排序:
How Do Input Attributes Impact the Privacy Loss in Differential Privacy?
arXiv
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arXiv 2022年
作者: Mueller, Tamara T. Kolek, Stefan Jungmann, Friederike Ziller, Alexander Usynin, Dmitrii Knolle, Moritz Rueckert, Daniel Kaissis, Georgios Institute of Artificial Intelligence in Medicine Technical University of Munich Germany Institute of Radiology Technical University of Munich Germany Department of Mathematics Ludwig Maximilian University of Munich Germany Department of Computing Imperial College London United Kingdom Institute for Machine Learning in Biomedical Imaging Helmholtz Zentrum Munich Germany
Differential privacy (DP) is typically formulated as a worst-case privacy guarantee over all individuals in a database. More recently, extensions to individual subjects or their attributes, have been introduced. Under...
来源: 评论
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT
arXiv
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arXiv 2022年
作者: Ellis, Sam Martinez Manzanera, Octavio E. Baltatzis, Vasileios Nawaz, Ibrahim Nair, Arjun Le Folgoc, Loïc Desai, Sujal Glocker, Ben Schnabel, Julia A. School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom Biomedical Image Analysis Group Imperial College London United Kingdom Department of Radiology University College London United Kingdom The Royal Brompton & Harefield NHS Foundation Trust United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Germany Faculty of Informatics Technical University of Munich Germany
Generative adversarial networks (GANs) are able to model accurately the distribution of complex, high-dimensional datasets, for example images. This characteristic makes high-quality GANs useful for unsupervised anoma... 详细信息
来源: 评论
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
arXiv
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arXiv 2024年
作者: Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States
Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using laten... 详细信息
来源: 评论
General Vision Encoder Features as Guidance in Medical Image Registration
arXiv
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arXiv 2024年
作者: Kögl, Fryderyk Reithmeir, Anna Sideri-Lampretsa, Vasiliki Machado, Ines Braren, Rickmer Rückert, Daniel Schnabel, Julia A. Zimmer, Veronika A. School of Medicine & Health Klinikum Rechts der Isar Technical University of Munich Germany School of Computation Information & Technology Technical University of Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany Munich Center for Machine Learning Germany Cancer Research UK Cambridge Institute University of Cambridge United Kingdom Department of Oncology University of Cambridge United Kingdom Partner Site Munich Germany School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Department of Computing Imperial College London United Kingdom
General vision encoders like DINOv2 and SAM have recently transformed computer vision. Even though they are trained on natural images, such encoder models have excelled in medical imaging, e.g., in classification, seg... 详细信息
来源: 评论
Body Fat Estimation from Surface Meshes using Graph Neural Networks
arXiv
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arXiv 2023年
作者: Mueller, Tamara T. Zhou, Siyu Starck, Sophie Jungmann, Friederike Ziller, Alexander Aksoy, Orhun Movchan, Danylo Braren, Rickmer Kaissis, Georgios Rueckert, Daniel Institute for AI in Medicine and Healthcare Faculty of Informatics Technical University of Munich Germany Department of Diagnostic and Interventional Radiology Faculty of Medicine Technical University of Munich Germany Department of Computing Imperial College London United Kingdom Institute for Machine Learning in Biomedical Imaging Helmholtz-Zentrum Munich Germany
Body fat volume and distribution can be a strong indication for a person’s overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estima... 详细信息
来源: 评论
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models
arXiv
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arXiv 2024年
作者: Daum, Deniz Osuala, Richard Riess, Anneliese Kaissis, Georgios Schnabel, Julia A. Di Folco, Maxime Technical University of Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany Departament de Matemàtiques i Informàtica Universitat de Barcelona Spain Helmholtz AI Helmholtz Munich Germany Department of Computing Imperial College London United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom
Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging. This study addresses these challeng... 详细信息
来源: 评论
Extended Graph Assessment Metrics for Graph Neural Networks
arXiv
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arXiv 2023年
作者: Mueller, Tamara T. Starck, Sophie Feiner, Leonhard F. Bintsi, Kyriaki-Margarita Rueckert, Daniel Kaissis, Georgios Institute for AI in Medicine and Healthcare Faculty of Informatics Technical University of Munich Germany Department of Diagnostic and Interventional Radiology Faculty of Medicine Technical University of Munich Germany BioMedIA Department of Computing Imperial College London United Kingdom Institute for Machine Learning in Biomedical Imaging Helmholtz-Zentrum Munich Germany
When re-structuring patient cohorts into so-called population graphs, initially independent patients can be incorporated into one interconnected graph structure. This population graph can then be used for medical down... 详细信息
来源: 评论
Physics-Aware Motion Simulation for T2*-Weighted Brain MRI
arXiv
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arXiv 2023年
作者: Eichhorn, Hannah Hammernik, Kerstin Spieker, Veronika Epp, Samira M. Rueckert, Daniel Preibisch, Christine Schnabel, Julia A. Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany School of Computation Information and Technology Technical University of Munich Germany School of Medicine Technical University of Munich Germany Graduate School of Systemic Neurosciences Ludwig-Maximilians-University Germany Department of Computing Imperial College London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom
In this work, we propose a realistic, physics-aware motion simulation procedure for T2*-weighted magnetic resonance imaging (MRI) to improve learning-based motion correction. As T2*-weighted MRI is highly sensitive to... 详细信息
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Membership Inference Attacks Against Semantic Segmentation Models
arXiv
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arXiv 2022年
作者: Chobola, Tomas Usynin, Dmitrii Kaissis, Georgios Department of Informatics Technical University of Munich Munich Germany Artificial Intelligence in Medicine and Healthcare Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Institute for Machine Learning in Biomedical Imaging Helmholtz Zentrum München Munich Germany
Membership inference attacks aim to infer whether a data record has been used to train a target model by observing its predictions. In sensitive domains such as healthcare, this can constitute a severe privacy violati... 详细信息
来源: 评论
What do we learn? Debunking the Myth of Unsupervised Outlier Detection
arXiv
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arXiv 2022年
作者: Bercea, Cosmin I. Rueckert, Daniel Schnabel, Julia A. Faculty of Informatics Technical University of Munich Germany Institute of Machine Learning in Biomedical Imaging Helmholtz Center Munich Germany Helmholtz AI Helmholtz Munich Ingolstädter Landstraße 1 NeuherbergD-85764 Germany Klinikum Rechts der Isar Munich Germany Imperial College London United Kingdom School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom
Even though auto-encoders (AEs) have the desirable property of learning compact representations without labels and have been widely applied to out-of-distribution (OoD) detection, they are generally still poorly under... 详细信息
来源: 评论