7 papers
UNIStainNet: Foundation-Model-Guided Virtual Staining of H&E to IHC
Jillur Rahman Saurav, Thuong Le Hoai Pham, Pritam Mukherjee +3
Virtual immunohistochemistry (IHC) staining from hematoxylin and eosin (H&E) images can accelerate diagnostics by providing preliminary molecular insight directly from routine sect…
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…
A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT
Tanjin Taher Toma, Tejas Sudharshan Mathai, Bikash Santra +11
Accurate segmentation of pheochromocytoma (PCC) in abdominal CT scans is essential for tumor burden estimation, prognosis, and treatment planning. It may also help infer genetic cl…
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…
Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost
Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee +3
Purpose: To automate contrast phase classification in CT using organ-specific features extracted from a widely used segmentation tool with a lightweight decision tree classifier. M…
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.…