42 citations · 57 across the 5 of their papers we have counts for
5 papers
Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +4
Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large…
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +4
Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by…
Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)
Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze +2
Objective assessment of Magnetic Resonance Imaging (MRI) scans of osteoarthritis (OA) can address the limitation of the current OA assessment. Segmentation of bone, cartilage, and…
Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy
Jacky C. K. Chow, Steven K. Boyd, Derek D. Lichti +1
Fluoroscopic imaging that captures X-ray images at video framerates is advantageous for guiding catheter insertions by vascular surgeons and interventional radiologists. Visualizin…
Modelling Errors in X-ray Fluoroscopic Imaging Systems Using Photogrammetric Bundle Adjustment With a Data-Driven Self-Calibration Approach
Jacky C. K. Chow, Derek Lichti, Kathleen Ang +3
X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluoroscopic imaging can further acquire X-ray images at video frame rates, thus enabling non-invasive in-vivo mo…