Abstract: Does Bone Suppression and Lung Detection Improve Chest Disease Classification?

Chest radiography is the most common clinical examination type. To improve the quality of patient care and to reduce workload, researchers started developing methods for automatic pathology classification. In our paper [1], we investigate the effect of advanced image processing techniques – initially developed to support radiologists – on the performance of deep learning techniques.

Authors and Affiliations

1. Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Deutschland

   Ivo M. Baltruschat, Leonhard A. Steinmeister, Harald Ittrich, Gerhard Adam & Tobias Knopp

2. Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Deutschland

   Ivo M. Baltruschat & Tobias Knopp

3. DAISYLabs, Forschungszentrum Medizintechnik Hamburg, Hamburg, Deutschland

   Ivo M. Baltruschat & Leonhard A. Steinmeister

4. Philips Research, Hamburg, Deutschland

   Ivo M. Baltruschat, Hannes Nickisch, Axel Saalbach, Jens von Berg & Michael Grass

Corresponding author

Correspondence to Ivo M. Baltruschat .

Editors and Affiliations

1. Universität zu Lübeck, Institut für Medizinische Informatik, Lübeck, Germany

   Heinz Handels

2. Peter-L. Reichertz Inst. f. Med. Inf., Technische Univ. Braunschweig, Braunschweig, Niedersachsen, Germany

   Thomas M. Deserno

3. Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität, Erlangen, Germany

   Andreas Maier

4. Medical Image Computing, E230, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

   Klaus Hermann Maier-Hein

5. Fak. f. Informatik u. Mathematik, OTH Regensburg, Regensburg, Germany

   Christoph Palm

6. Inst. f. Med. Informatik, Charité - Universitätsmedizin Berlin, Berlin, Berlin, Germany

   Thomas Tolxdorff

Reprints and permissions

© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

Policies and ethics