30 citations · 32 across the 8 of their papers we have counts for
9 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…
CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents
Timothy Ossowski, Xinchi Liu, Danyal Maqbool +6
Clinical reasoning agents based on large language models (LLMs) aim to automate tasks such as intensive care unit (ICU) monitoring and patient state tracking from electronic health…
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…
Deep Learning for Longitudinal Gross Tumor Volume Segmentation in MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer
Xin Tie, Weijie Chen, Zachary Huemann +3
Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. However, manual segmentation of the…
COMMA: A Communicative Multimodal Multi-Agent Benchmark
Timothy Ossowski, Danyal Maqbool, Jixuan Chen +3
The rapid advances of multimodal agents built on large foundation models have largely overlooked their potential for language-based communication between agents in collaborative ta…
Nuclear Medicine AI in Action: The Bethesda Report (AI Summit 2024)
Arman Rahmim, Tyler J. Bradshaw, Guido Davidzon +10
The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various commu…
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…
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,…