Catharina Silvia Lisson

According to our database, Catharina Silvia Lisson authored at least 11 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

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

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

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

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