Peer-reviewedCC BYJournal of medical imaging (Bellingham, Wash.)
Contrastive learning and advanced bundle metrics outperform fractional anisotropy for Alzheimer’s disease diffusion MRI prediction: findings from the Beyond FA challenge
12 affiliations
- 1Vanderbilt University , Department of Electrical and Computer Engineering, Nashville, Tennessee,
- 2Vanderbilt University , Department of Computer Science, Nashville, Tennessee,
- 3Université Paris-Panthéon-Assas ; Paris,
- 4Vanderbilt University , Department of Biomedical Engineering, Nashville, Tennessee,
- 5Indian Institute of Technology Madras , Chennai,
- 6Universidad de Valladolid , Valladolid,
- 7Sano Centre for Computational Medicine , Kraków,
- 8Université de Sherbrooke ; Sherbrooke Connectivity Imaging Laboratory (SCIL), Informatics Department, Science Faculty, Sherbrooke, Quebec,
- 9Creighton University School of Medicine , Omaha, Nebraska,
- 10Indiana University , Luddy School of Informatics, Computing, and Engineering, Bloomington, Indiana,
- 11University of Southern California , Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Los Angeles, California,
- 12Vanderbilt University Institute of Imaging Science , Vanderbilt University Medical Center, Nashville, Tennessee,
Related
- PRInter: an inductive multimodal heterogeneous network embedding framework with contrastive learning for lncRNA-protein interaction prediction
- Sensitivity of physeal-metaphyseal tractography correlations with height velocity for fractional anisotropy threshold selection.
- RNA-guided contrastive learning enhances patient-level prediction from histology
- MIFF-CLIP-HCC: Contrastive Language-Image Pretraining-enhanced multimodal information fusion framework for early recurrence prediction in postoperative hepatocellular carcinoma.
- Explainable AI framework for Alzheimer’s disease prediction using multimodal data
BibTeX
@article{mcmaster2026contrastive,
title = {{Contrastive learning and advanced bundle metrics outperform fractional anisotropy for Alzheimer’s disease diffusion MRI prediction: findings from the Beyond FA challenge}},
author = {Elyssa McMaster and Nancy Newlin and Trent Schwartz and Adam Saunders and Gaurav Rudravaram and Jiheng Li and André Hucke and Yehyun Suh and Jongyeon Yoon and Eleanor Liard and Michael Kim and Chenyu Gao and Karthik Ramadass and Chloe Cho and Sriram Gnana Sambanthan and Tomasz Pieciak and Dominika Ciupek and Santiago Aja-Fernández and Antonio Tristán-Vega and Maciej Malawski and Alex Caron and Antoine Théberg and Francois Reault and Arnaud Boré and Manon Edde and Ji Kim and Yihao Liu and Lianrui Zuo and Eleftherios Garyfallidis and Talia Nir and Neda Jahanshad and Kurt Schilling and Daniel Moyer and Bennett A. Landman},
journal = {Journal of medical imaging (Bellingham, Wash.)},
year = {2026},
month = sep,
url = {https://europepmc.org/article/PMC/PMC13671859},
note = {CC BY},
}
RIS
TY - JOUR
TI - Contrastive learning and advanced bundle metrics outperform fractional anisotropy for Alzheimer’s disease diffusion MRI prediction: findings from the Beyond FA challenge
AU - Elyssa McMaster
AU - Nancy Newlin
AU - Trent Schwartz
AU - Adam Saunders
AU - Gaurav Rudravaram
AU - Jiheng Li
AU - André Hucke
AU - Yehyun Suh
AU - Jongyeon Yoon
AU - Eleanor Liard
AU - Michael Kim
AU - Chenyu Gao
AU - Karthik Ramadass
AU - Chloe Cho
AU - Sriram Gnana Sambanthan
AU - Tomasz Pieciak
AU - Dominika Ciupek
AU - Santiago Aja-Fernández
AU - Antonio Tristán-Vega
AU - Maciej Malawski
AU - Alex Caron
AU - Antoine Théberg
AU - Francois Reault
AU - Arnaud Boré
AU - Manon Edde
AU - Ji Kim
AU - Yihao Liu
AU - Lianrui Zuo
AU - Eleftherios Garyfallidis
AU - Talia Nir
AU - Neda Jahanshad
AU - Kurt Schilling
AU - Daniel Moyer
AU - Bennett A. Landman
PY - 2026
DA - 2026/09/01
JO - Journal of medical imaging (Bellingham, Wash.)
UR - https://europepmc.org/article/PMC/PMC13671859
SN - 2329-4302
SN - 2329-4310
ER -