Search Results for author: Felix Denzinger

Found 17 papers, 3 papers with code

An unobtrusive quality supervision approach for medical image annotation

no code implementations11 Nov 2022 Sonja Kunzmann, Mathias Öttl, Prathmesh Madhu, Felix Denzinger, Andreas Maier

Users could not detect 52. 12% of generated images by DM proofing the feasibility to replace the original cells with synthetic cells without being noticed.

whole slide images

On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting

1 code implementation2 Nov 2022 Fabian Wagner, Mareike Thies, Laura Pfaff, Oliver Aust, Sabrina Pechmann, Daniela Weidner, Noah Maul, Maximilian Rohleder, Mingxuan Gu, Jonas Utz, Felix Denzinger, Andreas Maier

In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data.

Computed Tomography (CT) Image Denoising +1

Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT

no code implementations15 Jul 2022 Fabian Wagner, Mareike Thies, Felix Denzinger, Mingxuan Gu, Mayank Patwari, Stefan Ploner, Noah Maul, Laura Pfaff, Yixing Huang, Andreas Maier

Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.

Computed Tomography (CT) Denoising

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

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.

Automatic Plane Adjustment of Orthopedic Intra-operative Flat Panel Detector CT-Volumes

no code implementations15 Sep 2021 Celia Martin Vicario, Florian Kordon, Felix Denzinger, Jan Siad El Barbari, Maxim Privalov, Jochen Franke, Sarina Thomas, Lisa Kausch, Andreas Maier, Holger Kunze

The most important benefit of the MTL approach is that it is a single network for standard plane regression for all body regions with a reduced number of stored parameters.

Multi-Task Learning regression

Coronary Plaque Analysis for CT Angiography Clinical Research

no code implementations11 Jan 2021 Felix Denzinger, Michael Wels, Christian Hopfgartner, Jing Lu, Max Schöbinger, Andreas Maier, Michael Sühling

However, to enable clinical research with the help of these algorithms, a software solution, which enables manual correction, comprehensive visual feedback and tissue analysis capabilities, is needed.

Segmentation

Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans

no code implementations13 Dec 2019 Felix Denzinger, Michael Wels, Katharina Breininger, Anika Reidelshöfer, Joachim Eckert, Michael Sühling, Axel Schmermund, Andreas Maier

Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research.

Management Texture Classification

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