Máté E. Maros

According to our database, Máté E. Maros authored at least 8 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Semantically Conditioned Diffusion Models for Cerebral DSA Synthesis.

Qiwen Xu, David Rügamer, Holger Wenz, Johann Fontana, Nora Meggyeshazi, Andreas Bender, Máté E. Maros

CoRR, February, 2026

Deep generative models in digital subtraction angiography (DSA) and X-ray angiography: a systematic review.

Qiwen Xu, David Rügamer, Andreas Bender, Máté E. Maros

Artif. Intell. Rev., February, 2026

2025

Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models' family with scarce data.

Claudio Benzoni, Martina Langhals, Martin Boeker, Luise Modersohn, Máté E. Maros

CoRR, September, 2025

Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data.

Nandhini Santhanam, Hee E. Kim, David Rügamer, Andreas Bender, Stefan Muthers, Chang Gyu Cho, Angelika Alonso, Kristina Szabo, Franz-Simon Centner, Holger Wenz, Thomas Ganslandt, Michael Platten, Christoph Groden, Michael Neumaier, Fabian Siegel, Máté E. Maros

npj Digit. Medicine, 2025

2022

Transfer learning for medical image classification: a literature review.

Hee E. Kim, Alejandro Cosa-Linan, Nandhini Santhanam, Mahboubeh Jannesari, Máté E. Maros, Thomas Ganslandt

BMC Medical Imaging, 2022

Verstehen was Ärzte schreiben: Kann KI die Datenflut in der Medizin bändigen?

Benedikt Kämpgen, Philipp Sodmann, Máté E. Maros, Andreas Klüter

Proceedings of the Künstliche Intelligenz im Gesundheitswesen: Entwicklungen, 2022

2021

PyDPLib: Python Differential Privacy Library for Private Medical Data Analytics.

Sana Imtiaz, Philipp Matthies, Francisco Pinto, Máté E. Maros, Holger Wenz, Ramin Sadre, Vladimir Vlassov

Proceedings of the IEEE International Conference on Digital Health, 2021

2020

Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methods.

Victor Saase, Holger Wenz, Thomas Ganslandt, Christoph Groden, Máté E. Maros

CoRR, 2020