Ischemic Stroke Lesion Segmentation

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Most implemented papers archive 2025-07-28

7 shown of 7 papers with code (23 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Boundary loss for highly unbalanced segmentation
  17 Dec 2018 5 repositories listed Syntology ran 2 of 26 samples · 24 unverified
  We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions.
• A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
  28 Mar 2024 2 repositories listed
  We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
• Acute and sub-acute stroke lesion segmentation from multimodal MRI
  31 Oct 2018 2 repositories listed
  Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed treatment decisions.
• Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
  2 Mar 2023 1 repository listed
  Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate.
• ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
  14 Jun 2022 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  The test dataset will be used for model validation only and will not be released to the public.
• Predicting Clinical Outcome of Stroke Patients with Tractographic Feature
  22 Jul 2019 1 repository listed
  However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients.
• Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
  4 Mar 2019 1 repository listed
  Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.