Meinrad Beer

According to our database, Meinrad Beer authored at least 19 papers between 2019 and 2026.

Collaborative distances:

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

2026

Abstract: Your other Left! Vision-language Models Fail to Understand Relative Positions in Medical Images.

Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin, Alex Bäuerle, Meinrad Beer, Michael Götz, Timo Ropinski

Proceedings of the Bildverarbeitung für die Medizin 2026 - Proceedings, German Conference on Medical Image Computing, Luebeck, March 15, 2026

Abstract: Minimum Data, Maximum Impact - 20 Annotated Samples for Explainable Lung Nodule Classification.

Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz

Proceedings of the Bildverarbeitung für die Medizin 2026 - Proceedings, German Conference on Medical Image Computing, Luebeck, March 15, 2026

2025

FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI.

Luisa Gallée, Yiheng Xiong, Meinrad Beer, Michael Götz

CoRR, November, 2025

Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification.

Luisa Gallée, Catharina Silvia Lisson, Meinrad Beer, Michael Götz

CoRR, February, 2025

Proto-Caps: interpretable medical image classification using prototype learning and privileged information.

Luisa Gallée, Catharina Silvia Lisson, Timo Ropinski, Meinrad Beer, Michael Götz

PeerJ Comput. Sci., 2025

Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images.

Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin, Alex Bäuerle, Meinrad Beer, Michael Götz, Timo Ropinski

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

Minimum Data, Maximum Impact: 20 Annotated Samples for Explainable Lung Nodule Classification.

Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz

Proceedings of the Interpretability of Machine Intelligence in Medical Image Computing, 2025

Abstract: Selective Reduction of CT Data for Self-supervised Pre-training Improves Downstream Classification Performance.

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski

Proceedings of the Bildverarbeitung für die Medizin 2025, 2025

Abstract: Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model.

Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz

Proceedings of the Bildverarbeitung für die Medizin 2025, 2025

2024

Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.

Christoph Gerhard Lisson, Michael Götz, Daniel Wolf, Sabitha Manoj, Luisa Gallée, Stefan A. Schmidt, Eugen Tausch, Christof Schneider, Stephan Stilgenbauer, Ambros J. Beer, Meinrad Beer, Nico Sollmann, Catharina Silvia Lisson

BMC Bioinform., December, 2024

Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance.

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski

Comput. Biol. Medicine, 2024

Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model.

Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz

Proceedings of the Explainable Artificial Intelligence, 2024

Abstract: Self-supervised Pre-training for Dealing with Small Datasets in Deep Learning for Medical Imaging - Evaluation of Contrastive and Masked Autoencoder Methods.

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski

Proceedings of the Bildverarbeitung für die Medizin 2024, 2024

Abstract: Interpretable Medical Image Classification Using Prototype Learning and Privileged Information.

Luisa Gallée, Meinrad Beer, Michael Götz

Proceedings of the Bildverarbeitung für die Medizin 2024, 2024

2023

Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images.

Patrick Thiam, Ludwig Lausser, Christopher Kloth, Daniel Blaich, Andreas Liebold, Meinrad Beer, Hans A. Kestler

Frontiers Artif. Intell., February, 2023

Dealing with Small Datasets for Deep Learning in Medical Imaging: An Evaluation of Self-Supervised Pre-Training on CT Scans Comparing Contrastive and Masked Autoencoder Methods for Convolutional Models.

Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Timo Ropinski, Michael Götz

CoRR, 2023

Interpretable Medical Image Classification Using Prototype Learning and Privileged Information.

Luisa Gallée, Meinrad Beer, Michael Götz

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2021

Improving COVID-19 CXR Detection with Synthetic Data Augmentation.

Daniel Schaudt, Christopher Kloth, Christian Spaete, Andreas Hinteregger, Meinrad Beer, Reinhold von Schwerin

CoRR, 2021

2019

Potential role of CT-textural features for differentiation between viral interstitial pneumonias, pneumocystis jirovecii pneumonia and diffuse alveolar hemorrhage in early stages of disease: a proof of principle.

Christopher Kloth, Wolfgang Maximilian Thaiss, Robert Beck, Michael Haap, Jan Fritz, Meinrad Beer, Marius Horger

BMC Medical Imaging, 2019