1 code implementation • 15 Apr 2024 • Tobias Weber, Jakob Dexl, David Rügamer, Michael Ingrisch
The application of Tucker decomposition to the TS model substantially reduced the model parameters and FLOPs across various compression rates, with limited loss in segmentation accuracy.
no code implementations • 2 Nov 2023 • Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
Materials and Methods: An orthogonalization is utilized to remove the influence of protected features (e. g., age, sex, race) in chest radiograph embeddings, ensuring feature-independent results.
no code implementations • 9 Jun 2023 • Alessandro Wollek, Philip Haitzer, Thomas Sedlmeyr, Sardi Hyska, Johannes Rueckel, Bastian Sabel, Michael Ingrisch, Tobias Lasser
In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler.
no code implementations • 9 Jun 2023 • Alessandro Wollek, Sardi Hyska, Bastian Sabel, Michael Ingrisch, Tobias Lasser
Deep learning models for image classification are often trained at a resolution of 224 x 224 pixels for historical and efficiency reasons.
no code implementations • 9 Jun 2023 • Alessandro Wollek, Sardi Hyska, Bastian Sabel, Michael Ingrisch, Tobias Lasser
Finally, we propose and evaluate WindowNet, a model that learns optimal window settings, and show that it significantly improves performance compared to the baseline model without windowing.
no code implementations • 5 Jun 2023 • Alessandro Wollek, Sardi Hyska, Thomas Sedlmeyr, Philip Haitzer, Johannes Rueckel, Bastian O. Sabel, Michael Ingrisch, Tobias Lasser
This study aimed to develop an algorithm to automatically extract annotations for chest X-ray classification models from German thoracic radiology reports.
1 code implementation • 25 May 2023 • Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
Undersampling is a common method in Magnetic Resonance Imaging (MRI) to subsample the number of data points in k-space, reducing acquisition times at the cost of decreased image quality.
2 code implementations • 20 Mar 2023 • Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail.
no code implementations • 30 Dec 2022 • Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber, Johanna Topalis, Tobias Weber, Philipp Wesp, Bastian Sabel, Jens Ricke, Michael Ingrisch
In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT.
1 code implementation • 1 Aug 2022 • Alessandro Wollek, Theresa Willem, Michael Ingrisch, Bastian Sabel, Tobias Lasser
The proposed IDV approach trained on ID (chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0. 999 OOD AUC across the three data sets, surpassing all other OOD detection methods.
no code implementations • 21 Oct 2021 • Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer
We propose a hazard-regularized variational autoencoder that supports straightforward interpretation of deep neural architectures in the context of survival analysis, a field highly relevant in healthcare.
no code implementations • 21 Oct 2021 • Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
The application of deep learning in survival analysis (SA) allows utilizing unstructured and high-dimensional data types uncommon in traditional survival methods.