22 citations · 43 across the 16 of their papers we have counts for
5 papers · 1 filter
MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance
Hallee E. Wong, Jose Javier Gonzalez Ortiz, John Guttag +1
Medical researchers and clinicians often need to perform novel segmentation tasks on a set of related images. Existing methods for segmenting a new dataset are either interactive,…
Learning General-Purpose Biomedical Volume Representations using Randomized Synthesis
Neel Dey, Benjamin Billot, Hallee E. Wong +5
Current volumetric biomedical foundation models struggle to generalize as public 3D datasets are small and do not cover the broad diversity of medical procedures, conditions, anato…
VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis
Andrew Hoopes, Neel Dey, Victor Ion Butoi +2
We present VoxelPrompt, an end-to-end image analysis agent that tackles free-form radiological tasks. Given any number of volumetric medical images and a natural language prompt, V…
Boosting Skull-Stripping Performance for Pediatric Brain Images
William Kelley, Nathan Ngo, Adrian V. Dalca +3
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the…
Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz +3
Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for most new segmentation task, a new model has to be trained or fine-tuned.…