Search Results for author: Oliver Taubmann

Found 9 papers, 0 papers with code

DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images

no code implementations26 May 2022 Simon Langer, Oliver Taubmann, Felix Denzinger, Andreas Maier, Alexander Mühlberg

Reliably detecting diseases using relevant biological information is crucial for real-world applicability of deep learning techniques in medical imaging.

Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection

no code implementations5 May 2022 Florian Thamm, Oliver Taubmann, Markus Jürgens, Aleksandra Thamm, Felix Denzinger, Leonhard Rist, Hendrik Ditt, Andreas Maier

The best configuration detects LVOs with an AUC of 0. 91, LVOs in the ICA with an AUC of 0. 96, and in the MCA with 0. 91 while accurately predicting the affected side.

Data Augmentation

An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes

no code implementations26 Apr 2022 Florian Thamm, Markus Jürgens, Oliver Taubmann, Aleksandra Thamm, Leonhard Rist, Hendrik Ditt, Andreas Maier

In the work at hand, we place the algorithm in a clinical context by evaluating the labeling and occlusion detection on stroke patients, where we have achieved labeling sensitivities comparable to other works between 92\,\% and 95\,\%.

Specificity

CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling

no code implementations8 Feb 2022 Felix Denzinger, Michael Wels, Oliver Taubmann, Mehmet A. Gülsün, Max Schöbinger, Florian André, Sebastian J. Buss, Johannes Görich, Michael Sühling, Andreas Maier, Katharina Breininger

With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high.

Detection of Large Vessel Occlusions using Deep Learning by Deforming Vessel Tree Segmentations

no code implementations3 Dec 2021 Florian Thamm, Oliver Taubmann, Markus Jürgens, Hendrik Ditt, Andreas Maier

Training the EfficientNetB1 architecture on 100 data sets, the proposed augmentation scheme was able to raise the ROC AUC to 0. 85 from a baseline value of 0. 56 using no augmentation.

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