9 citations · 15 across the 9 of their papers we have counts for
12 papers
Image-Based Breast Implant Detection for Mammography Dataset Curation and Near-Real-Time Deployment: Comparing Foundation Models and Task-Specific Convolutional Models
Vasisht Ishwar, Hari Trivedi, Young Seok Jeon +5
Purpose: To evaluate the performance-feasibility tradeoffs of foundation models (FMs) and task-specific convolutional neural networks (CNNs) trained from scratch for breast implant…
Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports
Aawez Mansuri, Kush Mehta, Mohammadreza Chavoshi +10
Converting free-text radiology reports into structured labels supports cohort building, quality assurance, and monitoring of clinical imaging models, but the strongest label extrac…
Multi-Site Real-World Performance of Commercial AI for Pulmonary and Incidental Pulmonary Embolism Detection
Aawez Mansuri, Mohammadreza Chavoshi, Theodorus Dapamede +13
Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet the real-world performance of FDA-cleared AI detection models remains incompletely characterized. We ret…
Solving the Needle-in-a-Haystack Problem in Mammography Vision-Language Model with Differentiable Subset Sampling
Young Seok Jeon, Beatrice Brown-Mulry, Rohan Satya Isaac +5
There is growing interest in adopting CLIP-style vision--language model (VLM) pretraining for mammography. However, models that directly employ the standard CLIP architecture and t…
RadHarmony: Radiological Data Handling in the Era of Agentic AI
Frank Li, Bardia Khosravi, Mohammadreza Chavoshi +5
Training deep learning models on radiological images requires integrating heterogeneous datasets across different sources, file formats, directory layouts, label schemas, and annot…
MultiMedVision: Multi-Modal Medical Vision Framework
Frank Li, Bardia Khosravi, Mohammadreza Chavoshi +5
Multi-modal medical imaging enables comprehensive diagnostics, yet current foundation models process 2D (e.g. X-ray) and 3D (e.g. CT) data with separate, dimensionality-specific ar…
Feature Quality and Adaptability of Medical Foundation Models: A Comparative Evaluation for Radiographic Classification and Segmentation
Frank Li, Theo Dapamede, Mohammadreza Chavoshi +12
Foundation models (FMs) promise to generalize medical imaging, but their effectiveness varies. It remains unclear how pre-training domain (medical vs. general), paradigm (e.g., tex…
Impact of Label Noise from Large Language Models Generated Annotations on Evaluation of Diagnostic Model Performance
Mohammadreza Chavoshi, Hari Trivedi, Janice Newsome +6
Large language models (LLMs) are increasingly used to generate labels from radiology reports to enable large-scale AI evaluation. However, label noise from LLMs can introduce bias…
Evaluating Vision Language Models (VLMs) for Radiology: A Comprehensive Analysis
Frank Li, Hari Trivedi, Bardia Khosravi +8
Foundation models, trained on vast amounts of data using self-supervised techniques, have emerged as a promising frontier for advancing artificial intelligence (AI) applications in…
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Jungkyu Park, Jan Witowski, Yanqi Xu +8
Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer scr…
Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk
Theodorus Dapamede, Aisha Urooj, Vedant Joshi +15
Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk…
Emory Knee Radiograph (MRKR) Dataset
Brandon Price, Jason Adleberg, Kaesha Thomas +6
The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 83,011 patients, 40% of which are African American. This da…