9 citations · 25 across the 32 of their papers we have counts for
37 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…
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…
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…
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…
Classification of Multi-Parametric Body MRI Series Using Deep Learning
Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm +2
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrec…
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.…
Longitudinal Assessment of Lung Lesion Burden in CT
Tejas Sudharshan Mathai, Benjamin Hou, Ronald M. Summers
In the U.S., lung cancer is the second major cause of death. Early detection of suspicious lung nodules is crucial for patient treatment planning, management, and improving outcome…
Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
Anisa V. Prasad, Tejas Sudharshan Mathai, Pritam Mukherjee +2
An accurate segmentation of the pancreas on CT is crucial to identify pancreatic pathologies and extract imaging-based biomarkers. However, prior research on pancreas segmentation…
Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
Peter D. Erickson, Tejas Sudharshan Mathai, Ronald M. Summers
Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion detection algorithms have been devel…
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…
3D Universal Lesion Detection and Tagging in CT with Self-Training
Jared Frazier, Tejas Sudharshan Mathai, Jianfei Liu +2
Radiologists routinely perform the tedious task of lesion localization, classification, and size measurement in computed tomography (CT) studies. Universal lesion detection and tag…
Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
Tejas Sudharshan Mathai, Sungwon Lee, Thomas C. Shen +2
Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distingu…
Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation
Xinya Wang, Tejas Sudharshan Mathai, Boah Kim +1
Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT studies can be acquired with low…
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…
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
Yan Zhuang, Tejas Sudharshan Mathai, Pritam Mukherjee +6
Background: Segmentation of organs and structures in abdominal MRI is useful for many clinical applications, such as disease diagnosis and radiotherapy. Current approaches have foc…
Shadow and Light: Digitally Reconstructed Radiographs for Disease Classification
Benjamin Hou, Qingqing Zhu, Tejas Sudarshan Mathai +3
In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises of 50,188 frontal Digitally…
Automated classification of multi-parametric body MRI series
Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm +1
Multi-parametric MRI (mpMRI) studies are widely available in clinical practice for the diagnosis of various diseases. As the volume of mpMRI exams increases yearly, there are conco…
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…
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
Qingqing Zhu, Xiuying Chen, Qiao Jin +6
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics,…
Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study
Andrew M. Nguyen, Jianfei Liu, Tejas Sudharshan Mathai +2
Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD). However, manual assessment of CAC often requires radiological expertise,…
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT
David C. Oluigboa, Bikash Santra, Tejas Sudharshan Mathai +6
Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize. For the management of patients with PPGLs, CT is the…
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim +3
Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocol…
Weakly-Supervised Detection of Bone Lesions in CT
Tao Sheng, Tejas Sudharshan Mathai, Alexander Shieh +1
The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However,…