Daniel Wolf, M.Sc.

Daniel Wolf works as a research associate in a cooperation between the Department of Radiology at the University Hospital of Ulm and the Visual Computing Research Group at the University of Ulm. He received his Master’s degree in Electrical Engineering with a focus on Deep Learning from the University of Ulm in 2020. Website: cooperation Radiology-VISCOM

Research interests

Deep Learning on Medical Images
Pre-Training of Deep Learning Models with Self-Supervised Learning (Contrastive Learning and Masked Autoencoders)
Data Augmentation

Teaching

Seminar Research Trends in Visual Computing: Pre-Training with Contrastive Learning [WT22/23]
Project Deep Learning for Graphics and Visualization [WT21/22, ST22 ,WT22/23]

Projects

Racoon: Responsible computer scientist of the Raccon project for the University Hospital Ulm Website Racoon

Publications

Peer-Reviewed

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
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
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
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
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
Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

Daniel Wolf, Sebastian Regnery, Rafal Tarnawski, Barbara Bobek-Billewicz, Michael Götz

Applied Sciences 2022
Multiple trajectory prediction with deep temporal and spatial convolutional neural networks

Jan Strohbeck, Vasileios Belagiannis, Johannes Müller, Marcel Schreiber, Martin Herrmann, Daniel Wolf, Michael Buchholz

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020

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Daniel Wolf, M.Sc.

Research interests

Teaching

Projects

Publications

Peer-Reviewed

Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance

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

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

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

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

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

Multiple trajectory prediction with deep temporal and spatial convolutional neural networks

Daniel Wolf