1 citations · 1 across the 4 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
cs.CV2026
Scaling 3D Visual Grounding in Abdominal CT
Sam Church, Danyal Maqbool, Joshua D. Warner +4
Visual grounding models can enhance radiology workflows by linking report findings to image regions. This is particularly valuable for 3D CT, where findings often occupy a tiny fra…
cs.CV2026
Opportunistic Promptable Segmentation: Leveraging Routine Radiological Annotations to Guide 3D CT Lesion Segmentation
Samuel Church, Joshua D. Warner, Danyal Maqbool +4
The development of machine learning models for CT imaging depends on the availability of large, high-quality, and diverse annotated datasets. Although large volumes of CT images an…