Alan B. McMillan, PhD, Principal Investigator of the Molecular Imaging/Magnetic Resonance Technology Lab (MIMRTL), recently delivered two talks emphasizing the growing impact of foundation models in medical imaging. On April 9, 2025, Dr. McMillan presented …
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Professor McMillan Named Co-Chair of the Healthcare AI Challenge
We are excited to share that Professor Alan McMillan, Professor of Clinical Health Sciences and Director of the Molecular Imaging/Magnetic Resonance Technology Laboratory (MIMRTL), has been named Co-Chair of the Healthcare AI Challenge alongside Dr. …
Professor McMillan Named Co-Director of ICTR Pilot Awards Program
We are pleased to announce that Dr. Alan McMillan, Professor of Clinical Health Sciences in the Department of Radiology and Director of the Molecular Imaging/Magnetic Resonance Technology Laboratory (MIMRTL), has been appointed Co-Director of the …
New Paper, BAE-ViT: A Vision Transformer for Efficient and Accurate Bone Age Estimation
The Molecular Imaging/Magnetic Resonance Technology Laboratory (MIMRTL) is excited to share a new publication in the journal Tomography, BAE-ViT: An Efficient Multimodal Vision Transformer for Bone Age Estimation, led by Jinnian Zhang, Weijie Chen, and …
New Study Alert: Embedding-Based AI for Medical Image Classification, A More Efficient Future?
A new arXiv preprint by collaborator Raj Hansini Khoiwal and Molecular Imaging/Magnetic Resonance Technology Laboratory (MIMRTL) PI Alan B. McMillan challenges conventional AI training paradigms by demonstrating that pre-trained image embeddings alone can achieve high-performance …
Evaluating Large Language Models for Technical MRI Expertise: A New Study from MIMRTL
A new arXiv preprint by Alan B. McMillan, PI of MIMRTL, investigates the performance of large language models (LLMs) in answering technical MRI questions, assessing their potential to provide expert-level guidance in real-world clinical settings. …
New paper published: Neural Network Architectures for Self-Supervised Body Part Regression Models with Automated Localized Segmentation Application
Dr. McMillan, Principal Investigator of the MIMRTL group, in collaboration with Michael Fei, currently a medical student at Creighton University, have published a new paper in the Journal of Imaging Informatics in Medicine (JIIM) titled …
New arXiv preprint – Enhancing Interpretability in Medical Imaging with Scalable Ensembles
MIMRTL team members, graduate student Weijie Chen and Principal Investigator Alan McMillan, have published a new preprint on arXiv titled “SASWISE-UE: Segmentation and Synthesis with Interpretable Scalable Ensembles for Uncertainty Estimation”. This work introduces a …
New arXiv Paper Published on Bridging the Semantic Gap in Retrieval-Augmented Generation.
A new arXiv preprint, authored by Arihan Yadav, an undergraduate researcher in the Molecular Imaging/Magnetic Resonance Technology Laboratory (MIMRTL), and Alan B. McMillan, the PI of MIMRTL, introduces a novel projection-based method for aligning embeddings …
New preprint released: MedImageInsight- An Open-Source Embedding Model for General Domain Medical Imaging
Researchers from the Molecular Imaging Magnetic Resonance Technology Laboratory (MIMRTL) have contributed to a groundbreaking preprint that introduces MedImageInsight, an open-source generalist medical imaging model. The preprint, now available on arXiv, outlines the model’s ability …