Christoph Gerhard Lisson

According to our database, Christoph Gerhard Lisson authored at least 7 papers between 2023 and 2025.

Collaborative distances:

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

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, August, 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

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