aceAMSSE-SwinTrans: A harmonized multimodal hybrid framework with explainable AI for ischemic stroke lesion segmentation
BackgroundAccurate ischemic stroke lesion segmentation from magnetic resonance imaging (MRI) is important but challenging due to variability in lesion morphology. Traditional convolutional neural networks (CNNs) effectively capture local spatial features but often struggle to capture long-range contextual dependencies needed for irregular lesions. Transformer-based architectures are good at capturing global context; however, they are computationally expensive and difficult to deploy in resource-constrained envir…
A lightweight hybrid model segmented ischemic stroke lesions across MRI modalities with an overall Dice of 0.8629 at low compute cost, with visual explanations to support clinical interpretation.
The model was evaluated on two-dimensional MRI slices from two public datasets, and the text reports no prospective or real-world clinical deployment testing.
Evidence
- Peer-reviewedTechnology and Health Care2026-10-10
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Truvace Impact Record TRV-2026-1633, v1: “AMSSE-SwinTrans: A harmonized multimodal hybrid framework with explainable AI for ischemic stroke lesion segmentation.” Truvace, 2026-10-17. /record/TRV-2026-1633 (accessed at citation time). sha256 9c5cfab675205530…
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