A. Saalbach

发表

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Michael Grass, Hannes Nickisch, Tobias Knopp, 2018, Scientific Reports.

Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network

A. Saalbach, T. Brosch, A. Saalbach, 2020, American Journal of Neuroradiology.

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data

B. Arnrich, H. Schulz, A. Saalbach, 2022, 2205.03168.

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data

B. Arnrich, H. Schulz, A. Saalbach, 2022, Sensors.

Tubular shape aware data generation for segmentation in medical imaging

D. Dylov, H. Schulz, A. Saalbach, 2022, International Journal of Computer Assisted Radiology and Surgery.

Optimal C-arm angulation during transcatheter aortic valve replacement: Accuracy of a rotational C-arm computed tomography based three dimensional heart model

J. Weese, A. Saalbach, J. Balzer, 2016, World journal of cardiology.

Tubular shape aware data generation for segmentation in medical imaging

D. Dylov, H. Schulz, A. Saalbach, 2020, International Journal of Computer Assisted Radiology and Surgery.

EEG Coherence Visualization with Self-Organizing Maps

Helge Ritter, Axel Saalbach, Sabine Weiss, 2006 .

Libraries of synthetic stationary-phase and stress promoters as a tool for fine-tuning of expression of recombinant proteins in Escherichia coli.

Axel Saalbach, Thorsten Twellmann, Tim W Nattkemper, 2005, Journal of biotechnology.

Automatic Multi-model-Based Segmentation of the Left Atrium in Cardiac MRI Scans

Helko Lehmann, Jürgen Weese, Olaf Dössel, 2012, MICCAI.

Unsupervised clustering of fMRI and MRI time series

Anke Meyer-Bäse, Axel Wismüller, Axel Saalbach, 2007, Biomed. Signal Process. Control..

Continual Learning for Domain Adaptation in Chest X-ray Classification

Axel Saalbach, Heinrich Schulz, Matthias Lenga, 2020, MIDL.

Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNN

Jens von Berg, Astrid Franz, Axel Saalbach, 2019, 1908.08329.

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Ivo M. Baltruschat, H. Nickisch, M. Grass, 2018, Scientific Reports.

Detection Of Foreign Objects In Chest Radiographs Using Deep Learning

Tim Harder, Axel Saalbach, Thomas Buelow, 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops).

Image fusion for dynamic contrast enhanced magnetic resonance imaging

Axel Saalbach, Thorsten Twellmann, Tim W Nattkemper, 2004, Biomedical engineering online.

A rapid reporter system using GFP as a reporter protein for identification and screening of synthetic stationary-phase promoters in Escherichia coli

T. W. Nattkemper, A. Saalbach, E. Flaschel, 2006, Applied Microbiology and Biotechnology.

Semi-automatic acquisition and labelling of image data using SOMs

Helge J. Ritter, Gunther Heidemann, Axel Saalbach, 2003, ESANN.

When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification?

Michael Grass, Jens von Berg, Hannes Nickisch, 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation

Hannes Nickisch, Axel Saalbach, Ivo M. Baltruschat, 2020, European Radiology.

Image Fusion Based on Topographic Mappings Using the Hyperbolic Space

Helge J. Ritter, Tim W. Nattkemper, Axel Saalbach, 2005, Inf. Vis..

Foveal fully convolutional nets for multi-organ segmentation

Axel Saalbach, Tom Brosch, T. Brosch, 2018, Medical Imaging.

Reagent-free automatic cell viability determination using neural networks based machine vision and dark-field microscopy in Saccharomyces cerevisiae

Ning Wei, A. Saalbach, T.W. Nattkemper, 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

Image fusion for dynamic contrast enhanced magnetic resonance imaging.

M. Leach, T. Nattkemper, A. Saalbach, 2004 .

On the application of (topographic) independent and tree-dependent component analysis for the examination of DCE-MRI data

Anke Meyer-Bäse, Tim W. Nattkemper, Axel Saalbach, 2009, Biomed. Signal Process. Control..

Analysis of breast MRI data based on (topographic) independent and tree-dependent component analysis

Axel Saalbach, Oliver Lange, Anke Meyer-Baese, 2007, SPIE Defense + Commercial Sensing.

Detection of suspicious lesions in dynamic contrast enhanced MRI data

T. Twellmann, A. Saalbach, C. Muller, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Heterogeneity of kinetic curve parameters as indicator for the malignancy of breast lesions in DCE MRI

Thomas Bülow, Rafael Wiemker, Axel Saalbach, 2010, Medical Imaging.

Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs pdf

Nathan Cross, André Gooßen, Axel Saalbach, 2019, ArXiv.

Localization of Critical Findings in Chest X-Ray Without Local Annotations Using Multi-Instance Learning

Axel Saalbach, Hrishikesh Deshpande, Evan Schwab, 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

A Hyperbolic Topographic Mapping for Proximity Data

Helge J. Ritter, Axel Wismüller, Tim W. Nattkemper, 2005, Artificial Intelligence and Applications.

Multiscale entropy analysis in dynamic contrast-enhanced MRI

Martin O. Leach, Tim W. Nattkemper, Axel Saalbach, 2004, SPIE Medical Imaging.

Tubular shape aware data generation for segmentation in medical imaging pdf

Steffen Renisch, Axel Saalbach, Heinrich Schulz, 2020, International Journal of Computer Assisted Radiology and Surgery.

Automated anatomy detection in CT localizer images

Axel Saalbach, Martin Bergtholdt, Thomas Netsch, 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

Evaluation of multiparameter micrograph analysis with synthetical benchmark images

Axel Saalbach, Thorsten Twellmann, Tim Wilhelm Nattkemper, 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

Identification of Cell Viability by an In-situ Dark-fieldMicroscopic Sensor

T. Nattkemper, A. Saalbach, T. Twellmann, 2005 .