30 citations · 32 across the 8 of their papers we have counts for
4 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…
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…
PETAR: Localized Findings Generation with Mask-Aware Vision-Language Modeling for PET Automated Reporting
Danyal Maqbool, Changhee Lee, Zachary Huemann +11
Generating automated reports for 3D positron emission tomography (PET) is an important and challenging task in medical imaging. PET plays a vital role in oncology, but automating r…
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
Zachary Huemann, Xin Tie, Junjie Hu +1
Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning,…