58 citations · 70 across the 9 of their papers we have counts for
10 papers
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…
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…
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…
RadRotator: 3D Rotation of Radiographs with Diffusion Models
Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani +4
Transforming two-dimensional (2D) images into three-dimensional (3D) volumes is a well-known yet challenging problem for the computer vision community. In the medical domain, a few…
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Farhad Maleki, Linda Moy, Reza Forghani +14
Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of…
Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
Bardia Khosravi, Frank Li, Theo Dapamede +8
Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, partic…
Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
Pouria Rouzrokh, Bardia Khosravi, Shahriar Faghani +3
Despite the ever-increasing interest in applying deep learning (DL) models to medical imaging, the typical scarcity and imbalance of medical datasets can severely impact the perfor…