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2020 – today
- 2025
[j4]Luisa Gallée
, Catharina Silvia Lisson, Timo Ropinski, Meinrad Beer, Michael Götz:
Proto-Caps: interpretable medical image classification using prototype learning and privileged information. PeerJ Comput. Sci. 11: e2908 (2025)- [c7]Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz:
Abstract: Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. Bildverarbeitung für die Medizin 2025: 75 - [c6]Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski:
Abstract: Selective Reduction of CT Data for Self-supervised Pre-training Improves Downstream Classification Performance. Bildverarbeitung für die Medizin 2025: 284 - [c5]Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin, Alex Bäuerle, Meinrad Beer, Michael Götz, Timo Ropinski:
Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images. MICCAI (5) 2025: 691-701 - [i9]Luisa Gallée, Catharina Silvia Lisson, Meinrad Beer, Michael Götz:
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification. CoRR abs/2502.08997 (2025) - [i8]Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin, Alex Bäuerle, Meinrad Beer, Michael Götz, Timo Ropinski:
Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images. CoRR abs/2508.00549 (2025) - [i7]Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz:
Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification. CoRR abs/2508.00639 (2025) - [i6]Luisa Gallée, Yiheng Xiong, Meinrad Beer, Michael Götz:
FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI. CoRR abs/2511.15481 (2025) - 2024
- [j3]Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski:
Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance. Comput. Biol. Medicine 183: 109242 (2024) - [c4]Luisa Gallée, Meinrad Beer, Michael Götz:
Abstract: Interpretable Medical Image Classification Using Prototype Learning and Privileged Information. Bildverarbeitung für die Medizin 2024: 25 - [c3]Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski:
Abstract: Self-supervised Pre-training for Dealing with Small Datasets in Deep Learning for Medical Imaging - Evaluation of Contrastive and Masked Autoencoder Methods. Bildverarbeitung für die Medizin 2024: 157 - [c2]Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz:
Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. xAI (1) 2024: 43-56 - [i5]Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz:
Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. CoRR abs/2404.09917 (2024) - [i4]Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski:
Less is More: Selective Reduction of CT Data for Self-Supervised Pre-Training of Deep Learning Models with Contrastive Learning Improves Downstream Classification Performance. CoRR abs/2410.14524 (2024) - 2023
- [j2]Patrick Thiam, Ludwig Lausser, Christopher Kloth, Daniel Blaich, Andreas Liebold, Meinrad Beer, Hans A. Kestler:
Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images. Frontiers Artif. Intell. 6 (2023) - [c1]Luisa Gallée, Meinrad Beer, Michael Götz:
Interpretable Medical Image Classification Using Prototype Learning and Privileged Information. MICCAI (2) 2023: 435-445 - [i3]Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Timo Ropinski, Michael Götz:
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. CoRR abs/2308.06534 (2023) - [i2]Luisa Gallée, Meinrad Beer, Michael Götz:
Interpretable Medical Image Classification using Prototype Learning and Privileged Information. CoRR abs/2310.15741 (2023) - 2021
- [i1]Daniel Schaudt, Christopher Kloth, Christian Spaete, Andreas Hinteregger, Meinrad Beer, Reinhold von Schwerin:
Improving COVID-19 CXR Detection with Synthetic Data Augmentation. CoRR abs/2112.07529 (2021)
2010 – 2019
- 2019
- [j1]Christopher Kloth, Wolfgang Maximilian Thaiss, Robert Beck, Michael Haap, Jan Fritz, Meinrad Beer, Marius Horger:
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. BMC Medical Imaging 19(1): 39:1-39:9 (2019)
Coauthor Index
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