Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
666-04-004 ISMRM Abstract

Improving the Accuracy of Fetal Brain Age Prediction Using Neural Network Attribution Maps

Primary: Body - Fetal
Secondary: Analysis Methods - Classification and Prediction
666-04-004 · Neuroimaging of Brain Development from Fetal Life Through Adolescence: Structure, Function, and Prediction · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row G
Keywords: Atlas Brain Age Estimation Fetal brain MRI Fetal Development Attribution map
Accepted
Hongjia Yang1, Mingxuan Liu1, Yi Liao2, Muye Zhang1, Juncheng Zhu2, Haoxiang Li1, Fenglin Jia2, Zihan Li1, YIJIN LI1, Junwei Huang 1, Ziang Wang1, Ziyu Li3, Haibo Qu2, Qiyuan Tian1
1Tsinghua University, Beijing, China
2Sichuan University, West China Second University Hospital, Chengdu, China
3Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
Presenting Author: Junwei Huang

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Yun HJ, Lee HJ, You S, et al. Deep Learning–based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly. Radiology: Artificial Intelligence. 2025;7(2):e240115. doi:10.1148/ryai.240115 [doi]
2. Shi W, Yan G, Li Y, et al. Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty. NeuroImage. 2020;223:117316. doi:10.1016/j.neuroimage.2020.117316 [doi]
3. Arun N, Gaw N, Singh P, et al. Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging. Radiology: Artificial Intelligence. Published online October 6, 2021. doi:10.1148/ryai.2021200267 [doi]
4. Hong J, Yun HJ, Park G, et al. Optimal Method for Fetal Brain Age Prediction Using Multiplanar Slices From Structural Magnetic Resonance Imaging. Front Neurosci. 2021;15. doi:10.3389/fnins.2021.714252 [doi]
5. Ebner M, Wang G, Li W, et al. An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage. 2020;206:116324. doi:10.1016/j.neuroimage.2019.116324 [doi]
6. Liu M, Li H, Li Z, et al. Image Quality Assessment using an Orientation Recognition Network for Fetal MRI. In: 2024 ISMRM annual meeting. 2024:0726.
7. Peng H, Gong W, Beckmann CF, Vedaldi A, Smith SM. Accurate brain age prediction with lightweight deep neural networks. Medical Image Analysis. 2021;68:101871. doi:10.1016/j.media.2020.101871 [doi]
8. Pang Y, Liang J, Huang T, et al. Slim UNETR: Scale Hybrid Transformers to Efficient 3D Medical Image Segmentation Under Limited Computational Resources. IEEE Transactions on Medical Imaging. 2024;43(3):994-1005. doi:10.1109/TMI.2023.3326188 [doi]
9. Smith SM, Vidaurre D, Alfaro-Almagro F, Nichols TE, Miller KL. Estimation of brain age delta from brain imaging. NeuroImage. 2019;200:528-539. doi:10.1016/j.neuroimage.2019.06.017 [doi]
10. Fidon L, Aertsen M, Kofler F, et al. A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2024;46(5):3784-3795. doi:10.1109/TPAMI.2023.3346330 [doi]
11. Gholipour A, Rollins CK, Velasco-Annis C, et al. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci Rep. 2017;7(1):1-13. doi:10.1038/s41598-017-00525-w [doi]
12. Peng P, Zhang Y, Wang H, Zhang H. Towards robust and understandable fault detection and diagnosis using denoising sparse autoencoder and smooth integrated gradients. ISA Transactions. 2022;125:371-383. doi:10.1016/j.isatra.2021.06.005 [doi]
13. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Int J Comput Vis. 2020;128(2):336-359. doi:10.1007/s11263-019-01228-7 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/666-04-004