26 citations · 51 across the 12 of their papers we have counts for
9 papers · 1 filter
Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR
Sheethal Bhat, Bogdan Georgescu, Awais Mansoor +5
Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object detection methods leverage grou…
Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond
Sheethal Bhat, Bogdan Georgescu, Adarsh Bhandary Panambur +8
Detecting abnormalities in medical images poses unique challenges due to differences in feature representations and the intricate relationship between anatomical structures and abn…
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features
Saahil Islam, Venkatesh N. Murthy, Dominik Neumann +5
To restore proper blood flow in blocked coronary arteries via angioplasty procedure, accurate placement of devices such as catheters, balloons, and stents under live fluoroscopy or…
Self-Supervised Learning for Interventional Image Analytics: Towards Robust Device Trackers
Saahil Islam, Venkatesh N. Murthy, Dominik Neumann +5
An accurate detection and tracking of devices such as guiding catheters in live X-ray image acquisitions is an essential prerequisite for endovascular cardiac interventions. This i…
ConTrack: Contextual Transformer for Device Tracking in X-ray
Marc Demoustier, Yue Zhang, Venkatesh Narasimha Murthy +2
Device tracking is an important prerequisite for guidance during endovascular procedures. Especially during cardiac interventions, detection and tracking of guiding the catheter ti…
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…
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…