Catharina Silvia Lisson

14 papers Journal 8Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	—	`conf`	Abstract: Minimum Data, Maximum Impact - 20 Annotated Samples for Explainable Lung Nodule Classification. Bildverarbeitung für die Medizin Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz
2025	—	`conf`	Abstract: Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. Bildverarbeitung für die Medizin Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz
2025	—	`conf`	Abstract: Selective Reduction of CT Data for Self-supervised Pre-training Improves Downstream Classification Performance. Bildverarbeitung für die Medizin Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski
2025	J	`jnl`	Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification. CoRR Luisa Gallée, Catharina Silvia Lisson, Meinrad Beer, Michael Götz
2025	—	`conf`	Minimum Data, Maximum Impact: 20 Annotated Samples for Explainable Lung Nodule Classification. iMIMIC@MICCAI Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz
2025	J	`jnl`	Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification. CoRR Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz
2025	J	`jnl`	Proto-Caps: interpretable medical image classification using prototype learning and privileged information. PeerJ Comput. Sci. Luisa Gallée, Catharina Silvia Lisson, Timo Ropinski, Meinrad Beer, Michael Götz
2024	—	`conf`	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 Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski
2024	—	`conf`	Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. xAI (1) Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz
2024	J	`jnl`	Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. CoRR Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz
2024	J	`jnl`	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 Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski
2024	J	`jnl`	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 Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski
2024	J	`jnl`	Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology. BMC Bioinform. 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
2023	J	`jnl`	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 Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Timo Ropinski, Michael Götz