openmedical/registry
← registry

VS_Seg

imported

software/vs-seg

Automatic Segmentation of Vestibular Schwannoma with MONAI (PyTorch)

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

record
Category
Software & Systems
Subcategory
unknown
License
Apache-2.0(osi)
Status
maintained
Maturity
deployed
Organization
KCL-BMEIS
Country
unknown
Homepage
unknown
Repository
github.com/KCL-BMEIS/VS_Seg
Documentation
unknown
Tags
deep-learning · medical-image-segmentation · monai · pytorch
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.

  • MONAILabelmonai · pytorch

    MONAI Label is an intelligent open source image labeling and learning tool.

  • MRISkullStrippingmonai · pytorch

    Developing a UNet3D model for accurate MRI skull stripping using the Calgary Campinas 359 dataset, enhancing neuroimaging preprocessing workflows.

  • active-segmentationmedical-image-segmentation · pytorch

    ActiveSegmentation: A Simulation Framework for Benchmarking Active Learning Strategies for 3D Medical Image Segmentation

  • CBIM-Medical-Image-Segmentationmedical-image-segmentation · pytorch

    A PyTorch framework for medical image segmentation

  • CMU-Netmedical-image-segmentation · pytorch

    [ISBI 2023] Official Pytorch implementation of "CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network"

  • DAEFormermedical-image-segmentation · pytorch

    [MICCAI 2023] DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation

sources
  1. api.github.com/repos/KCL-BMEIS/VS_Seg
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-01-06, 49 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/50.json→ .entries["vs-seg"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.