7 citations · 7 across the 5 of their papers we have counts for
5 papers
A Unified 2D Framework for DeepLesion Detection, Segmentation and Short Report Generation
Ruida Cheng, Tejas S. Mathai, Benjamin Hou +4
In previous work, we integrated large language models (LLMs) into the lesion segmentation model based on the ULS23 DeepLesion dataset, using short-form findings from the reports. I…
Text Embedded Swin-UMamba for DeepLesion Segmentation
Ruida Cheng, Tejas Sudharshan Mathai, Pritam Mukherjee +5
Segmentation of lesions on CT enables automatic measurement for clinical assessment of chronic diseases (e.g., lymphoma). Integrating large language models (LLMs) into the lesion s…
LEAVS: An LLM-based Labeler for Abdominal CT Supervision
Ricardo Bigolin Lanfredi, Yan Zhuang, Mark Finkelstein +7
Extracting structured labels from radiology reports has been employed to create vision models to simultaneously detect several types of abnormalities. However, existing works focus…
How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses
Qingqing Zhu, Benjamin Hou, Tejas S. Mathai +7
Automatically interpreting CT scans can ease the workload of radiologists. However, this is challenging mainly due to the scarcity of adequate datasets and reference standards for…
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
Ricardo Bigolin Lanfredi, Pritam Mukherjee, Ronald Summers
In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in la…