collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV2026

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…

cs.CV2026

CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography

Qingqing Zhu, Qiao Jin, Tejas S. Mathai +10

Artificial intelligence (AI) can automatically delineate lesions on computed tomography (CT) and generate radiology report content, yet progress is limited by the scarcity of publi…

cs.CV2025

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…

cs.CV2025

Utility of Pancreas Surface Lobularity as a CT Biomarker for Opportunistic Screening of Type 2 Diabetes

Tejas Sudharshan Mathai, Anisa V. Prasad, Xinya Wang +6

Type 2 Diabetes Mellitus (T2DM) is a chronic metabolic disease that affects millions of people worldwide. Early detection is crucial as it can alter pancreas function through morph…

cs.CV2025

Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI

Nicole Tran, Anisa Prasad, Yan Zhuang +6

The segmentation of multiple organs in multi-parametric MRI studies is critical for many applications in radiology, such as correlating imaging biomarkers with disease status (e.g.…

cs.CV2025

Correcting Class Imbalances with Self-Training for Improved Universal Lesion Detection and Tagging

Alexander Shieh, Tejas Sudharshan Mathai, Jianfei Liu +2

Universal lesion detection and tagging (ULDT) in CT studies is critical for tumor burden assessment and tracking the progression of lesion status (growth/shrinkage) over time. Howe…