Exploring Multimodal Large Language Models for Radiology Report Error-checking

no code implementations • 20 Dec 2023 • Jinge Wu, Yunsoo Kim, Eva C. Keller, Jamie Chow, Adam P. Levine, Nikolas Pontikos, Zina Ibrahim, Paul Taylor, Michelle C. Williams, Honghan Wu

This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports.

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

1 code implementation • 7 Apr 2022 • Simon Dahan, Hao Xu, Logan Z. J. Williams, Abdulah Fawaz, Chunhui Yang, Timothy S. Coalson, Michelle C. Williams, David E. Newby, A. David Edwards, Matthew F. Glasser, Alistair A. Young, Daniel Rueckert, Emma C. Robinson

Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice.

Classification Data Augmentation

Whole Heart Anatomical Refinement from CCTA using Extrapolation and Parcellation

no code implementations • 18 Nov 2021 • Hao Xu, Steven A. Niederer, Steven E. Williams, David E. Newby, Michelle C. Williams, Alistair A. Young

In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e. g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle.

Anatomy Segmentation