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2020 – today
- 2025
[c3]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- 2024
- [j1]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) - [c2]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 - [c1]Hannah Kniesel, Leon Sick, Tristan Payer, Tim Bergner, Kavitha Shaga Devan, Clarissa Read, Paul Walther, Timo Ropinski, Pedro Hermosilla:
Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images. ICLR 2024 - [i3]Sebastian Hartwig, Dominik Engel, Leon Sick, Hannah Kniesel, Tristan Payer, Poonam Poonam, Michael Glöckler, Alex Bäuerle, Timo Ropinski:
Evaluating Text-to-Image Synthesis: Survey and Taxonomy of Image Quality Metrics. CoRR abs/2403.11821 (2024) - [i2]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
- [i1]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)
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