mlsanityData to 14 Oct 2026

Transformers-based architectures for stroke segmentation: A review

arXiv 2403.18637 ↗First posted 27 Mar 2024eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes. The emergence of deep learning methodologies has transformed the landscape of medical image analysis. Recently, Transformers, initially designed for natural language processing, have exhibited remarkable capabilities in various computer vision applications, including medical image analysis. This comprehensive review aims to provide an in-depth exploration of the cutting-edge Transformer-based architectures applied in the context of stroke segmentation. It commences with an exploration of stroke pathology, imaging modalities, and the challenges associated with accurate diagnosis and segmentation. Subsequently, the review delves into the fundamental ideas of Transformers, offering detailed insights into their architectural intricacies and the underlying mechanisms that empower them to effectively capture complex spatial information within medical images. The existing literature is systematically categorized and analyzed, discussing various approaches that leverage Transformers for stroke segmentation. A critical assessment is provided, highlighting the strengths and limitations of these methods, including considerations of performance and computational efficiency. Additionally, this review explores potential avenues for future research and development

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 67 of its 133 references that are in the map. First posted on arXiv 27 Mar 2024.

Builds on

67 of 133 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusioncited by 2
  2. 2025Stroke Locus Net: Occluded Vessel Localization from MRI Modalitiescited by 0
  3. 2026Automated Lesion Segmentation of Stroke MRI Using nnU-Net: A Comprehensive External Validation Across Acute and Chronic Lesionscited by 0
  4. 2026ISLA: A U-Net for MRI-based acute ischemic stroke lesion segmentation with deep supervision, attention, domain adaptation, and ensemble learningcited by 0
  5. 2026Energy-Mamba: A Physics-Constrained State-Space Model for Medical Image Classificationcited by 0