30 citations · 32 across the 6 of their papers we have counts for
7 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…
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…
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…
Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Network
Xin Tie, Muheon Shin, Changhee Lee +10
: Automatic quantification of longitudinal changes in PET scans for lymphoma patients has proven challenging, as residual disease in interim-therapy scans is ofte…
Automatic Personalized Impression Generation for PET Reports Using Large Language Models
Xin Tie, Muheon Shin, Ali Pirasteh +8
In this study, we aimed to determine if fine-tuned large language models (LLMs) can generate accurate, personalized impressions for whole-body PET reports. Twelve language models w…