Andreea Bianca Popescu

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection

Andreea Bianca Popescu, Andreas Seitz, M. Becker, Heiko Mahrholdt et autres

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CONSENTIS - An Innovative Framework for Identity and Consent Management for EU Digital and Data Strategies

Nikos Kyriakoulis, Charis Dimopoulos, George Daniil, Vassilis Prevelakis et autres

For many years now, the management of personal information and consent for access and usage remain among the top priorities and challenges in the online world. Individuals demand better control, transparency, and security over their personal data and companies need clear processes …

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Automatic selection of optimal TI for flow‐independent dark‐blood delayed‐enhancement MRI

Andreea Bianca Popescu, Wolfgang Rehwald, David C. Wendell, Céleste Chevalier et autres

Abstract Purpose Propose and evaluate an automatic approach for predicting the optimal inversion time (TI) for dark and gray blood images for flow‐independent dark‐blood delayed‐enhancement (FIDDLE) acquisition based on free‐breathing FIDDLE TI‐scout images. Methods In 267 patients, the TI‐scout sequence acquired single‐shot …

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Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection

Andreea Bianca Popescu, Andreas Seitz, Heiko Mahrholdt, Jens Wetzl et autres

Objectives Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual delineation. Traditional approaches relying on average relaxation values and single cutoffs may oversimplify myocardial complexity. This study evaluates whether deep learning (DL) can achieve segmentation …

Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI

Cosmin-Andrei Hatfaludi, Aurelian Roșca, Andreea Bianca Popescu, Teodora Chițiboi et autres

Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to identify the optimal cardiovascular magnetic resonance imaging (CMRI) views for distinguishing between normal and …

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Deep Learning-based Segmentation for Automatic Prediction of Optimal Inversion Time of Flow Independent Dark Blood Delayed Enhancement (FIDDLE) Images

Andreea Bianca Popescu, Wolfgang Rehwald, Bogdan Andrei Gheorghita, Lucian Itu et autres

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Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition

Andreea Bianca Popescu, Cosmin Nita, Ioana Antonia Taca, Anamaria Vizitiu et autres

Deep learning (DL)-based solutions have been extensively researched in the medical domain in recent years, enhancing the efficacy of diagnosis, planning, and treatment. Since the usage of health-related data is strictly regulated, processing medical records outside the hospital environment for developing and …

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Non-bijectivity-based image obfuscation method for deep learning based medical applications

Andreea Bianca Popescu, Cosmin Nita, Ioana Antonia Taca, Anamaria Vizitiu et autres

As more and more deep learning (DL) solutions are employed in the healthcare domain using the Machine Learning as a Service (MLaaS) paradigm, concerns regarding personal data privacy have been raised. In this context, especially in medical imaging, the demand for privacy-preserving …

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Obfuscation Algorithm for Privacy-Preserving Deep Learning-Based Medical Image Analysis

Andreea Bianca Popescu, Ioana Antonia Taca, Anamaria Vizitiu, Cosmin Nita et autres

Deep learning (DL)-based algorithms have demonstrated remarkable results in potentially improving the performance and the efficiency of healthcare applications. Since the data typically needs to leave the healthcare facility for performing model training and inference, e.g., in a cloud based solution, privacy …

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Privacy Preserving Classification of EEG Data Using Machine Learning and Homomorphic Encryption

Andreea Bianca Popescu, Ioana Antonia Taca, Cosmin Nita, Anamaria Vizitiu et autres

Data privacy is a major concern when accessing and processing sensitive medical data. A promising approach among privacy-preserving techniques is homomorphic encryption (HE), which allows for computations to be performed on encrypted data. Currently, HE still faces practical limitations related to high …

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