most cited3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware

1 citations · 1 across the 1 of their papers we have counts for

collaborators

2 papers

eess.IV2021★ 1 cited

3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware

Keno K. Bressem, Stefan M. Niehues, Bernd Hamm +3

Segmentation of pulmonary infiltrates can help assess severity of COVID-19, but manual segmentation is labor and time-intensive. Using neural networks to segment pulmonary infiltra…

cs.LG2020

Comparing Different Deep Learning Architectures for Classification of Chest Radiographs

Keno K. Bressem, Lisa Adams, Christoph Erxleben +3

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify che…