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)

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)

Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images. - Hannah Kniesel; Leon Sick; Tristan Payer; Tim Bergner; Kavitha Shaga Devan; Clarissa Read; Paul Walther; Timo Ropinski; Pedro Hermosilla (ICLR, 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 Text-to-Image Synthesis: Survey and Taxonomy of Image Quality Metrics. - Sebastian Hartwig; Dominik Engel; Leon Sick; Hannah Kniesel; Tristan Payer; Poonam Poonam; Michael Glöckler; Alex Bäuerle; Timo Ropinski (CoRR, 2024)

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)