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AMSSE-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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1633· cite & verify
AMSSE-SwinTrans: A harmonized multimodal hybrid framework with explainable AI for ischemic stroke lesion segmentation
"Cerebral Folate Deficiency - Cerebral CT-scan at 4 years old" by Sarah Mafi, Pierre-Antoine Faye is licensed under CC BY 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.

In brief

Researchers built AMSSE-SwinTrans, a hybrid model that pairs a convolutional network with simplified transformer blocks and an adaptive multi-scale squeeze-and-excitation module. It was trained and tested on two-dimensional MRI slices drawn from the ATLAS v2.0 and ISLES 2022 datasets to outline ischemic stroke lesions.

Accurate lesion outlining matters because stroke lesions vary in shape, and heavy or opaque models are hard to deploy and trust in clinics. The reported results are from retrospective dataset evaluation, so how the tool performs in live clinical workflows remains untested in the supplied text.

Main points

  1. The model reached an overall Dice score of 0.8629 and a lesion-specific Dice score of 0.7535.
  2. It uses 27.67 M parameters, 17.67 GFLOPs and 9.22 ms inference time per slice.
  3. An ablation study showed performance improved as each component was added.
  4. Attention Rollout and Grad-CAM++ visualizations are used to support interpretability.

The gain

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 rundown

The design targets known weaknesses: convolutional networks capture local features but struggle with long-range context for irregular lesions, while full transformers are computationally expensive for resource-constrained settings.

Evaluation used single-channel harmonized multimodal two-dimensional MRI slices from ATLAS v2.0 and ISLES 2022. Model comparisons were reported as significant using Wilcoxon signed-rank tests with p < 0.001.

The authors conclude that the framework is resource-efficient and interpretable for multimodal stroke lesion segmentation, citing explainable AI visualizations as a way to address the black-box concern that limits clinical adoption.

Sources

  1. Peer-reviewedTechnology and Health Care2026-10-10

The debate