Comparing different deep learning architectures for classification of chest radiographs
Keno K. Bressem(TUM Klinikum), Janis L. Vahldiek(Humboldt-Universität zu Berlin), Lisa C. Adams(TUM Klinikum), Bernd Hamm(European Society of Radiology), Stefan M. Niehues(Charité - Universitätsmedizin Berlin), Christoph Erxleben(Charité - Universitätsmedizin Berlin)
Refubium (Universitätsbibliothek der Freien Universität Berlin)
January 1, 2020
Cited by 170
Related Papers
Global, regional, and national prevalence of adult overweight and obesity, 1990–2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021
|The Lancet|2025|801
SenNet recommendations for detecting senescent cells in different tissues
|Nature Reviews Molecular Cell Biology|2024|330
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
|Insights into Imaging|2024|263