Leng, S;
Zong, D;
Yang, P;
Hu, M;
Tan, RS;
Sia, CH;
Teo, L;
... Zhong, L; + view all
(2025)
Multi-Scale Attention Network for Myocardial Infarction Transmurality Classification in Late Gadolinium Enhancement CMR.
In:
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
IEEE: Copenhagen, Denmark.
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Abstract
The transmural extent of hyperenhancement on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is a key marker of myocardial infarction severity and prognosis. Current visual assessment methods suffer from inter-observer variability and reliance on manual segmentation. In this paper, we propose a Multi-Scale Attention Network for Transmurality Classification (MSAN-TC) using LGE CMR images. MSAN-TC integrates convolutional neural networks (CNNs) and Transformer models with feature pyramid networks (FPN) and channel attention (CA) mechanisms, enabling accurate classification of infarction extent from weakly labeled data. Evaluated on 1,821 images from 315 patients, MSAN-TC achieves an overall accuracy of 86% in transmurality classification and an area under the curve (AUC) of 0.90 in detecting ≥50% transmural infarction, demonstrating high sensitivity (91%) and specificity (89%). This work represents a step towards automated, efficient, and clinically practical myocardial infarction assessment, providing a scalable solution for real-world applications.
| Type: | Proceedings paper |
|---|---|
| Title: | Multi-Scale Attention Network for Myocardial Infarction Transmurality Classification in Late Gadolinium Enhancement CMR |
| Event: | 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
| Location: | United States |
| Dates: | 14 Jul 2025 - 18 Jul 2025 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/EMBC58623.2025.11254580 |
| Publisher version: | https://doi.org/10.1109/embc58623.2025.11254580 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Humans, Myocardial Infarction, Gadolinium, Magnetic Resonance Imaging, Neural Networks, Computer, Contrast Media, Algorithms, Image Processing, Computer-Assisted, Sensitivity and Specificity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Pre-clinical and Fundamental Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10219944 |
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