By Dr Sebastiano Barbieri

Conference Abstracts

Barbieri S; Steiger P; Kruse A; Ith M; Thoeny H, 2017, 'DW-MRI as an alternative to biopsy in renal allograft patients with deteriorating renal function: a preliminary study', in European Congress of Radiology, Vienna, Austria, presented at European Congress of Radiology, Vienna, Austria

Munz J; Boxler S; Barbieri S; Thoeny H, 2017, 'Multiparametric MRI (mpMRI) of the prostate based on PI-RADS version 2: detection rate and negative predictive value', in Swiss Congress of Radiology, Bern, Switzerland, presented at Swiss Congress of Radiology, Bern, Switzerland

Barbieri S; Brönnimann M; Boxler S; Thoeny H, 2016, 'Detecting High-Risk Prostate Cancer by Analysing the Histogram of ADC and IVIM Parameter Values', in Swiss Congress of Radiology, Davos, Switzerland, presented at Swiss Congress of Radiology, Davos, Switzerland

Barbieri S; Donati O; Froehlich J; Thoeny H, 2016, 'Impact of Vendor and Field Strength of the Magnetic Resonance Scanner on Measurements of IVIM Parameter Values in Abdominal Organs', in Swiss Congress of Radiology, Davos, Switzerland, presented at Swiss Congress of Radiology, Davos, Switzerland

Klein J; Weiler F; Barbieri S; Hirsch J; Geisler B; Hahn H, 2012, 'Novel Features of NeuroQLab - A Software Assistant for Evaluating Neuroimaging Data', in European Congress of Radiology, Vienna, Austria, presented at European Congress of Radiology, Vienna, Austria

Klein J; Weiler F; Barbieri S; Hirsch J; Geisler B; Hahn H, 2011, 'NeuroQLab - An Extendible Software Assistant for Efficient and Reproducible Evaluation of Neuroimaging Data', in European Congress of Radiology, Vienna, Austria, presented at European Congress of Radiology, Vienna, Austria

Bauer M; Egger J; Kuhnt D; Barbieri S; Klein J; Hahn H; Freisleben B; Nimsky C, 2010, 'Ein semi-automatischer graphbasierter Ansatz zur Bestimmungen des Randes von eloquenten Faserverbindungen des menschlichen Gehirns', in 44th DGBMT Annual Meeting, Rostock, Germany, presented at 44th DGBMT Annual Meeting, Rostock, Germany

Bauer M; Egger J; Kuhnt D; Barbieri S; Freisleben B; Nimsky C, 2010, 'Evaluation of Several Cost Functions for Min-Cut-Segmentation of Fiber Bundles in the Human Brain', in 61st Annual Meeting of the German Society of Neurosurgery (DGNC), Mannheim, Germany, presented at Neurowoche, Mannheim, Germany

Bauer M; Egger J; Barbieri S; Klein J; Kuhnt D; Hahn H; Freisleben B; Nimsky C, 2010, 'Ray-Based and Graph-Based Methods for Fiber Bundle Boundary Estimation', in Biosignal 2010, Berlin, Germany, presented at International Biosignal Processing Conference, Berlin, Germany

Preprints

Zhou G; Catic A; Shabestari M; Young M; Li C; Poppe K; Barbieri S, 2026, From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories, http://dx.doi.org/10.48550/arxiv.2603.06720

Kuppens D; Barbieri S; Berg DVD; Schouten P; Thoeny HC; Wennen M; Gurney-Champion OJ, 2024, Acquisition-Independent Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations, http://dx.doi.org/10.48550/arxiv.2412.20844

Kuo NI-H; Jorm L; Barbieri S, 2023, Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models, http://dx.doi.org/10.48550/arxiv.2303.12281

Kuo NI-H; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Kaiser R; Polizzotto M; Jorm L; Barbieri S, 2023, Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV, http://dx.doi.org/10.48550/arxiv.2208.08655

Kuo NI-H; Polizzotto MN; Finfer S; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Jorm L; Barbieri S, 2022, The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms, http://dx.doi.org/10.48550/arxiv.2203.06369

Kuo NI-H; Polizzotto M; Finfer S; Jorm L; Barbieri S, 2021, Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project, http://dx.doi.org/10.48550/arxiv.2112.03914

Liu J; Gallego B; Barbieri S, 2021, Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosis, http://dx.doi.org/10.48550/arxiv.2108.07392

Kaandorp MPT; Barbieri S; Klaassen R; van Laarhoven HWM; Crezee H; While PT; Nederveen AJ; Gurney-Champion OJ, 2021, Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients, http://dx.doi.org/10.48550/arxiv.2011.01689

Barbieri S; Mehta S; Wu B; Bharat C; Poppe K; Jorm L; Jackson R, 2020, Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach, http://dx.doi.org/10.48550/arxiv.2011.14032

Barbieri S; Kemp J; Perez-Concha O; Kotwal S; Gallagher M; Ritchie A; Jorm L, 2020, Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk, http://dx.doi.org/10.48550/arxiv.1905.08547

Barbieri S; Gurney-Champion OJ; Klaassen R; Thoeny HC, 2019, Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI, http://dx.doi.org/10.48550/arxiv.1903.00095

Bauer MHA; Barbieri S; Klein J; Egger J; Kuhnt D; Freisleben B; Hahn HK; Nimsky C, 2013, A Ray-based Approach for Boundary Estimation of Fiber Bundles Derived from Diffusion Tensor Imaging, http://dx.doi.org/10.48550/arxiv.1310.6092

Klein J; Barbieri S; Bauer MHA; Nimsky C; Hahn HK, 2011, Benchmarking the Quality of Diffusion-Weighted Images, http://dx.doi.org/10.48550/arxiv.1104.1556

Bauer MHA; Egger J; Kuhnt D; Barbieri S; Klein J; Hahn HK; Freisleben B; Nimsky C, 2011, Ray-Based and Graph-Based Methods for Fiber Bundle Boundary Estimation, http://dx.doi.org/10.48550/arxiv.1103.1952

Bauer MHA; Egger J; Kuhnt D; Barbieri S; Klein J; Hahn HK; Freisleben B; Nimsky C, 2011, A Semi-Automatic Graph-Based Approach for Determining the Boundary of Eloquent Fiber Bundles in the Human Brain, http://dx.doi.org/10.48550/arxiv.1103.1475

Carson JM; Barbieri S; Verich A; Tu E; Lloyd A; Dore GJ; Matthews GV; Martinello M, Development of a machine learning model to predict short duration HCV treatment response, http://dx.doi.org/10.1101/2025.09.19.25336147

Kuo NI-H; Perez-Concha O; Hanly M; Mnatzaganian E; Hao B; Di Sipio M; Yu G; Vanjara J; Valerie IC; de Oliveira Costa J; Churches T; Lujic S; Hegarty J; Jorm L; Barbieri S, Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project (Preprint), http://dx.doi.org/10.2196/preprints.51388

Marchesi R; Micheletti N; Kuo NI-H; Barbieri S; Jurman G; Osmani V, Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health Data, http://dx.doi.org/10.1101/2023.09.26.23296163

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