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active-segmentation

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software/active-segmentation

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

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active-segmentation project image
GitHub preview card for HealthML/active-segmentation. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
AGPL-3.0(osi)
Status
dormant
Maturity
deployed
Organization
HealthML
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
3d-image-segmentation · active-learning · deep-learning · medical-image-segmentation · medical-imaging · pytorch · pytorch-lightning · semantic-segmentation
Regulatory
unknown
built by · 6

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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  • MONAILabelactive-learning · medical-imaging · pytorch

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

  • mmsegmentationmedical-image-segmentation · pytorch · semantic-segmentation

    OpenMMLab Semantic Segmentation Toolbox and Benchmark.

  • medico-sammedical-image-segmentation · medical-imaging · semantic-segmentation

    Segment Anything for Medical Imaging

  • U-Net-PyTorchmedical-image-segmentation · medical-imaging · semantic-segmentation

    🧠 Implement U-Net in PyTorch for effective binary image segmentation, focusing on brain tumor detection with a complete pipeline from data prep to evaluation.

sources
  1. api.github.com/repos/HealthML/active-segmentation
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2022-07-05, 20 stars, license reported as AGPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/36.json→ .entries["active-segmentation"]

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