University of Wisconsin–Madison

Month: November 2024

MITRP Congratulates Eric Weber on Successfully Completing His Preliminary Examination!

We are pleased to announce that MITRP team member Eric Weber successfully passed his preliminary examination on November 26, 2024. This significant milestone marks his advancement to doctoral candidacy. Eric is co-advised by MITRP Principal Investigator Dr. Alan McMillan and by Dr. Nader Behdad. His examination committee also included Dr. Chu Ma and Dr. Kevin …

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. The Challenge: Variability in MRI Expertise Magnetic resonance imaging (MRI) is a powerful but technically complex imaging modality. Operator skill …

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 “Technical Note: Neural Network Architectures for Self-Supervised Body Part Regression Models with Automated Localized Segmentation Application.” This work presents advancements …

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 framework aimed at improving the interpretability and reliability of deep learning models in medical imaging. The Challenge of Interpretability in …