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, Cathrina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski

Computers in Biology and Medicine., 2024

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Medical volume segmentation by overfitting sparsely annotated data

Tristan Payer, Faraz Nizamani, Meinrad Beer, Michael Götz, Timo Ropinski

Journal of Medical Imaging, 2023

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Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

Daniel Wolf, Tristan Payer, Cathrina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski

Nature Scientific Reports, 2023

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CT Radiomics and Clinical Feature Model to Predict Lymph Node Metastases in Early-Stage Testicular Cancer

Cathrina Silvia Lisson, Sabitha Manoj, Daniel Wolf, Jasper Schrader, Stefan Andreas Schmidt, Meinrad Beer, Michael Götz, Friedemann Zengerling, Christoph Gerhard Lisson

Onco, 2023

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Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma

Cathrina Silvia Lisson, Christoph Gerhard Lisson, Marc Fabian Mezger, Daniel Wolf, Stefan Andreas Schmidt, Wolfgang Thaiss, Eugen Tausch, Ambros J. Beer, Stephan Stilgenbauer, Meinrad Beer, Michael Götz

Cancers, 2022

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Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)

Cathrina Silvia Lisson, Christoph Gerhard Lisson, Sherin Achilles, Marc Fabian Mezger, Daniel Wolf, Stefan Andreas Schmidt, Wolfgang Thaiss, Johannes Bloehdorn, Ambros J. Beer, Stephan Stilgenbauer, Meinrad Beer, Michael Götz

Cancers, 2022

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