26 citations · 89 across the 8 of their papers we have counts for
13 papers · 1 filter
Self-supervised Learning from 100 Million Medical Images
Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor +8
Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the cr…
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…
Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation
Zengming Shen, Yifan Chen, S. Kevin Zhou +3
The one-to-one mapping is necessary for many bidirectional image-to-image translation applications, such as MRI image synthesis as MRI images are unique to the patient. State-of-th…
One-to-one Mapping for Unpaired Image-to-image Translation
Zengming Shen, S. Kevin Zhou, Yifan Chen +3
Recently image-to-image translation has attracted significant interests in the literature, starting from the successful use of the generative adversarial network (GAN), to the intr…
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…