30 citations · 32 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision-Language Models
Qidong Wang, Junjie Hu, Ming Jiang
Recent work has increasingly explored neuron-level interpretation in vision-language models (VLMs) to identify neurons critical to final predictions. However, existing neuron analy…
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…