1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
Vision-Language Modeling in PET/CT for Visual Grounding of Positive Findings
Zachary Huemann, Samuel Church, Joshua D. Warner +7
Vision-language models can connect the text description of an object to its specific location in an image through visual grounding. This has potential applications in enhanced radi…
ResNCT: A Deep Learning Model for the Synthesis of Nephrographic Phase Images in CT Urography
Syed Jamal Safdar Gardezi, Lucas Aronson, Peter Wawrzyn +9
Purpose: To develop and evaluate a transformer-based deep learning model for the synthesis of nephrographic phase images in CT urography (CTU) examinations from the unenhanced and…