Michelle Livne

发表

A PET‐Guided Framework Supports a Multiple Arterial Input Functions Approach in DSC‐MRI in Acute Stroke

W. Heiss, K. Mouridsen, J. Sobesky, 2017, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

Multiparametric Model for Penumbral Flow Prediction in Acute Stroke

W. Heiss, K. Mouridsen, J. Sobesky, 2017, Stroke.

Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease

E. M. Akay, O. U. Aydin, J. Rieger, 2022, Frontiers in Neurology.

Boosted Tree Model Reforms Multimodal Magnetic Resonance Imaging Infarct Prediction in Acute Stroke

Michelle Livne, Kim Mouridsen, Jochen B. Fiebach, 2018, Stroke.

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Sam Blackwell, Mustafa Suleyman, O. Ronneberger, 2020, Journal of medical Internet research.

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease

John D. Kelleher, Dietmar Frey, Michelle Livne, 2019, Front. Neurosci..

Comparison of the 2 Most Popular Deconvolution Techniques for the Detection of Penumbral Flow in Acute Stroke

W. Heiss, J. Sobesky, V. Madai, 2015, Stroke.

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

Esra Zihni, Ivana Galinovic, Jochen B. Fiebach, 2019, PloS one.

BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

D. Frey, A. Hilbert, V. I. Madai, 2020, Frontiers in Artificial Intelligence.

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

Thilo Hagendorff, R. Zicari, J. Amann, 2022, PLOS digital health.

Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores

P. Vajkoczy, D. Frey, S. Wolf, 2021, Neurosurgical Review.

Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning

D. Moseley, Angela M. Gleason, T. DeWees, 2023, Frontiers in Oncology.

A precision medicine framework for personalized simulation of hemodynamics in cerebrovascular disease

Michelle Livne, Vince I. Madai, Dietmar Frey, 2020, BioMedical Engineering OnLine.

A novel approach for assessing hypoperfusion in stroke using spatial independent component analysis of resting‐state fMRI

D. Margulies, A. Villringer, T. Nierhaus, 2021, Human brain mapping.

A novel approach for assessing hypoperfusion in stroke using spatial independent component analysis of resting-state fMRI data

D. Margulies, A. Villringer, T. Nierhaus, 2020, bioRxiv.

Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks pdf

Ivana Galinovic, Michelle Livne, Anja Hennemuth, 2020, ArXiv.

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

Kristian Hildebrand, A. Hennemuth, J. Fiebach, 2022, Medical Image Anal..

Diagnostic and prognostic benefit of arterial spin labeling in subacute stroke

T. Liebig, S. Zweynert, J. Sobesky, 2019, Brain and behavior.

Multimodal Fusion Strategies for Outcome Prediction in Stroke

John D. Kelleher, J. Fiebach, I. Galinovic, 2020, HEALTHINF.

Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks

Ivana Galinovic, Dietmar Frey, Michelle Livne, 2021, Comput. Biol. Medicine.

A framework for testing different imputation methods for tabular datasets

Ivana Galinovic, Jochen B. Fiebach, Dietmar Frey, 2019, bioRxiv.

Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks

Kristian Hildebrand, A. Hennemuth, J. Fiebach, 2020, ArXiv.