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)

Improving COVID-19 CXR Detection with Synthetic Data Augmentation. - Daniel Schaudt; Christopher Kloth; Christian Spaete; Andreas Hinteregger; Meinrad Beer; Reinhold von Schwerin (CoRR, 2021)

Interpretable Medical Image Classification Using Prototype Learning and Privileged Information. - Luisa Gallée; Meinrad Beer; Michael Götz (MICCAI (2), 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)

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., 2023)

Abstract: Interpretable Medical Image Classification Using Prototype Learning and Privileged Information. - Luisa Gallée; Meinrad Beer; Michael Götz (Bildverarbeitung für die Medizin, 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 (Bildverarbeitung für die Medizin, 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 (xAI (1), 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)

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 (Bildverarbeitung für die Medizin, 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 (Bildverarbeitung für die Medizin, 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)

Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification. - Luisa Gallée; Catharina Silvia Lisson; Meinrad Beer; Michael Götz (CoRR, 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 (CoRR, 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 (CoRR, 2025)