24 citations · 24 across the 4 of their papers we have counts for
9 papers
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Fernando Navarro, Guido Sasahara, Suprosanna Shit +4
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For in…
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images
Giles Tetteh, Fernando Navarro, Johannes Paetzold +3
Collateral circulation results from specialized anastomotic channels which are capable of providing oxygenated blood to regions with compromised blood flow caused by ischemic injur…
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Fernando Navarro, Christopher Watanabe, Suprosanna Shit +4
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets. While current research focuses on creating novel pre…
Geometry-aware neural solver for fast Bayesian calibration of brain tumor models
Ivan Ezhov, Tudor Mot, Suprosanna Shit +9
Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression acco…
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection
Malek Husseini, Anjany Sekuboyina, Maximilian Loeffler +3
Osteoporotic vertebral fractures have a severe impact on patients' overall well-being but are severely under-diagnosed. These fractures present themselves at various levels of seve…
Deep Reinforcement Learning for Organ Localization in CT
Fernando Navarro, Anjany Sekuboyina, Diana Waldmannstetter +3
Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional im…