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- University of California, San FranciscoUS31 papers
- University of HoustonUS29 papers
- University of California, BerkeleyUS20 papers
- Space Telescope Science InstituteUS19 papers
- Lawrence Berkeley National LaboratoryUS18 papers
- Stanford UniversityUS17 papers
- Centre National de la Recherche ScientifiqueFR15 papers
- University of ArizonaUS14 papers
- The University of TokyoJP13 papers
- Jet Propulsion LaboratoryUS12 papers
- California Institute of TechnologyUS11 papers
- Princeton UniversityUS11 papers
9 papers · 1 filter
Are foundation models efficient for medical image segmentation?
Danielle Ferreira, Rima Arnaout
Foundation models are experiencing a surge in popularity. The Segment Anything model (SAM) asserts an ability to segment a wide spectrum of objects but required supervised training…
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Ariana M. Familiar, Anahita Fathi Kazerooni, Hannah Anderson +37
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the…
Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform
Nicholas Dwork, Daniel O'Connor, Ethan M. I. Johnson +4
The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities. Due to the non-unifo…
Semi-supervised learning for generalizable intracranial hemorrhage detection and segmentation
Emily Lin, Esther Yuh
Purpose: To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set. Materials…
multiMap: A Gradient Spoiled Sequence for Simultaneously Measuring B1+, B0, T1/M0, T2, T2*, and Fat Fraction of a Slice
Nicholas Dwork, Adam B. Kerr, Ethan M. I. Johnson +5
We propose multiMap, a single scan that can generate several quantitative maps simultaneously. The sequence acquires multiple images in a time-efficient manner, which can be modele…
Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries using Deep Learning with MRI Images
Nikan K. Namiri, Io Flament, Bruno Astuto +6
Purpose: To evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. Materials and Methods: Th…