12 citations · 12 across the 4 of their papers we have counts for
4 papers
Foundation model embeddings capture pre-diagnostic changes on screening mammograms
Kalina P. Slavkova, Eric Brattain, Aditya Gowd +6
Foundation model embeddings of screening mammograms may encode pre-diagnostic tissue change without task-specific adaptation. We tested whether embeddings move faster along a data-…
Reconfigurable Radiology Labels Without Relabeling
Jean-Benoit Delbrouck, Dave Van Veen, Akash Pattnaik +4
Public chest-radiograph (CXR) datasets are typically released with small, fixed label schemas such as CheXpert-14. However, the underlying free-text reports describe far more findi…
HOPPR Medical-Grade Platform for Medical Imaging AI
Kalina P. Slavkova, Melanie Traughber, Oliver Chen +5
Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text sa…
An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data
Kalina P. Slavkova, Julie C. DiCarlo, Viraj Wadhwa +5
The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerat…