6 papers
From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification
Xue Li, Jameson Merkow, Noel C. F. Codella +11
Foundation models provide robust embeddings for diverse tasks, including medical imaging. We evaluate embeddings from seven general and medical-specific foundation models (e.g., De…
Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
Zhijin He, Alan B. McMillan
The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpretation of radiological data. This…
Vision-Language Modeling in PET/CT for Visual Grounding of Positive Findings
Zachary Huemann, Samuel Church, Joshua D. Warner +7
Vision-language models can connect the text description of an object to its specific location in an image through visual grounding. This has potential applications in enhanced radi…
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis
Raj Hansini Khoiwal, Alan B. McMillan
Developing artificial intelligence (AI) and machine learning (ML) models for medical imaging typically involves extensive training and testing on large datasets, consuming signific…
Performance of Large Language Models in Technical MRI Question Answering: A Comparative Study
Alan B McMillan
Background: Advances in artificial intelligence, particularly large language models (LLMs), have the potential to enhance technical expertise in magnetic resonance imaging (MRI), r…
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
Noel C. F. Codella, Ying Jin, Shrey Jain +28
In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a dive…