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Philipp Sodmann

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Conference and Workshop Papers

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2022
[c5]
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  - electronic edition @ ceur-ws.org (open access)
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  dblp key:
  - conf/isbi/KrenzerSHP22
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  - https://dblp.org/rec/conf/isbi/KrenzerSHP22
Adrian Krenzer, Philipp Sodmann, Nico Hasler, Frank Puppe:
Deep Learning using temporal information for automatic polyp detection. EndoCV@ISBI 2022: 14-19
2021
[c4]
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  - conf/cinc/SodmannVK21
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  - https://dblp.org/rec/conf/cinc/SodmannVK21
Philipp Sodmann, Marcus Vollmer, Lars Kaderali:
Segment, Perceive and Classify - Multitask Learning of the Electrocardiogram in a Single Neural Network. CinC 2021: 1-4
2020
[c3]
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  - conf/cinc/SodmannV20
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  - https://dblp.org/rec/conf/cinc/SodmannV20
Philipp Sodmann, Marcus Vollmer:
ECG Segmentation Using a Neural Network as the Basis for Detection of Cardiac Pathologies. CinC 2020: 1-4
2019
[c2]
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  - conf/cinc/VollmerLSSK19
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Marcus Vollmer, Christian F. Luz, Philipp Sodmann, Bhanu Sinha, Sven-Olaf Kuhn:
Time-Specific Metalearners for the Early Prediction of Sepsis. CinC 2019: 1-4
2017
[c1]
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  - conf/cinc/VollmerSCNK17
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Marcus Vollmer, Philipp Sodmann, Leonard Caanitz, Neetika Nath, Lars Kaderali:
Can Supervised Learning Be Used to Classify Cardiac Rhythms?. CinC 2017

Parts in Books or Collections

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2022
[p1]
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  - books/sp/22/KampgenSMK22
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Benedikt Kämpgen, Philipp Sodmann, Máté E. Maros, Andreas Klüter:
Verstehen was Ärzte schreiben: Kann KI die Datenflut in der Medizin bändigen? Künstliche Intelligenz im Gesundheitswesen 2022: 547-564

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