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Issue DateTitleInvolved Person(s)
13-Apr-2018Aneurysm shape as a diagnostic toolJuchler, Norman; Schilling, Sabine; Rüfenacht, Daniel; Bijlenga, Philippe; Kurtcuoglu, Vartan, et al
8-Feb-2018Aneurysm shape as a diagnostic tool : a machine learning approachJuchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Rüfenacht, Daniel; Kurtcuoglu, Vartan, et al
30-Aug-2017Big Data : machine learning to identify shape biomarkers in intracranial aneurysmHirsch, Sven; Juchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Rüfenacht, Daniel
4-Oct-2018Clinical data sharing : a data scientist's perspectiveJuchler, Norman; Schilling, Sabine; Watanabe, Kazuhiro; Anzai, Hitomi; Rüfenacht, Daniel, et al
8-Feb-2018Correlation of CFD with wall enhancementAnzai, Hitomi; Juchler, Norman; Bilenga, Philippe; Rüfenacht, Daniel; Wanke, Isabel, et al
2018Effect of input image representation for results of neural network to detect cerebral aneurysmsWatanabe, Kazuhiro; Anzai, Hitomi; Juchler, Norman; Hirsch, Sven; Bijlenga, Philippe, et al
Jul-2019Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristicsDetmer, Felicitas J.; Hadad, Sara; Chung, Bong Jae; Mut, Fernando; Slawski, Martin, et al
30-Oct-2018External validation of cerebral aneurysm rupture probability model with data from two patient cohortsDetmer, Felicitas J.; Fajardo-Jiménez, Daniel; Mut, Fernando; Juchler, Norman; Hirsch, Sven, et al
2019Identification of clinically relevant characteristics of intracranial aneurysm morphologyJuchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Rüfenacht, Daniel; Kurtcuoglu, Vartan, et al
21-Jan-2020Influence of input image configurations on output of a convolutional neural network to detect cerebral aneurysmsWatanabe, Kazuhiro; Anzai, Hitomi; Juchler, Norman; Hirsch, Sven; Ohta, Makoto
7-Sep-2017Measuring the perceived morphological complexity of intracranial aneurysmsJuchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Kurtcuoglu, Vartan; Hirsch, Sven
15-Sep-2015On the utility of 3D Zernike Moment Invariants to assess aneurysm disease statusJuchler, Norman; Ebnöther, Ueli; Schilling, Sabine; Hirsch, Sven; Kurtcuoglu, Vartan
17-Mar-2020Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysmsJuchler, Norman; Schilling, Sabine; Glüge, Stefan; Bijlenga, Philippe; Rüfenacht, Daniel, et al
2019Real and assumed insights : statistical models and imaging biomarkers for disease characterization of intracranial aneurysmsHirsch, Sven; Juchler, Norman
12-Jul-2018Reproducing qualitative irregularity ratings by means of quantitative shape descriptors in intracranial aneurysmsJuchler, Norman; Schilling, Sabine; Philippe, Bijlenga; Rüfenacht, Daniel; Kurtcuoglu, Vartan, et al
15-Sep-2016Shape-based assessment of intracranial aneurysm disease statusJuchler, Norman; Schilling, Sabine; Vartan, Kurtcuoglu; Hirsch, Sven
1-Sep-2016Shape-based assessment of intracranial aneurysm disease status – a machine learning approachJuchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Kurtcuoglu, Vartan; Hirsch, Sven
9-Jun-2016Shape-based modeling of aneurysmal disease statusJuchler, Norman; Schilling, Sabine; Wanke, Isabel; Rüfenacht, Daniel; Bijlenga, Philippe, et al
2019Understanding morphological irregularity : a rater-based studyJuchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Rüfenacht, Daniel; Kurtcuoglu, Vartan, et al
Jan-2018Using machine learning to identify shape biomarkers in intracranial aneurysmHirsch, Sven; Juchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Rüfenacht, Daniel