4 papers
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…
SASWISE-UE: Segmentation and Synthesis with Interpretable Scalable Ensembles for Uncertainty Estimation
Weijie Chen, Alan McMillan
This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicabilit…
Mind the Gap: A Generalized Approach for Cross-Modal Embedding Alignment
Arihan Yadav, Alan McMillan
Retrieval-Augmented Generation (RAG) systems enhance text generation by incorporating external knowledge but often struggle when retrieving context across different text modalities…