20 citations · 20 across the 3 of their papers we have counts for
5 papers
Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
Sebastian Gündel, Arnaud A. A. Setio, Florin C. Ghesu +4
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of da…
Epoch-wise label attacks for robustness against label noise
Sebastian Guendel, Andreas Maier
The current accessibility to large medical datasets for training convolutional neural networks is tremendously high. The associated dataset labels are always considered to be the r…
Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment
Florin C. Ghesu, Bogdan Georgescu, Eli Gibson +6
The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater va…
Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels
Sebastian Guendel, Florin C. Ghesu, Sasa Grbic +4
Chest X-ray (CXR) is the most common X-ray examination performed in daily clinical practice for the diagnosis of various heart and lung abnormalities. The large amount of data to b…
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
Sebastian Guendel, Sasa Grbic, Bogdan Georgescu +4
Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve…