9 citations · 11 across the 8 of their papers we have counts for
11 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…
Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation
Raajitha Muthyala, Zhenan Yin, Alekhya Jilla +6
Frozen vision-transformer (ViT) foundation-model embeddings increasingly serve as the substrate for downstream chest-radiography (CXR) pipelines, yet where small-scale, low-contras…
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…
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…