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MSA-2Net

imported

software/msa-2net

[BMVC 2024] Official repository of the paper titled "MSA^2 Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation"

Machine-generated from the listed sources and not yet reviewed by a human.

MSA-2Net project image
GitHub preview card for xmindflow/MSA-2Net. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
maintained
Maturity
deployed
Organization
xmindflow
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
attention-mechanism · computer-vision · deep-learning · isic-2018 · medical-image-segmentation · receptive-field · synapse · transformers
Regulatory
unknown
built by · 2

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

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    Multimodal Brain mpMRI segmentation on BraTS 2023 and BraTS 2021 datasets.

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    [BMVC 2026 Oral] Gated Spatial Redundancy Projection for Pathology Transformer Attentions

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    LHU-Net: A Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Medical Image Segmentation

  • Source code and experiments for the paper: "Dark Corner on Skin Lesion Image Dataset: Does it matter?"

  • eye-diagnosis-attention-cnnattention-mechanism · computer-vision

    A deep learning framework for automated diagnosis of Diabetic Retinopathy, AMD, and Glaucoma using a Hybrid Attention-CNN model. Combines ResNet50 with self-attention for enhanced accuracy and…

sources
  1. api.github.com/repos/xmindflow/MSA-2Net
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2025-10-02, 71 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/2.json→ .entries["msa-2net"]

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