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DARTS

imported

software/darts

Code for DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

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

DARTS project image
GitHub preview card for NYUMedML/DARTS. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
GPL-3.0(osi)
Status
dormant
Maturity
deployed
Organization
NYUMedML
Country
unknown
Homepage
unknown
Repository
github.com/NYUMedML/DARTS
Documentation
unknown
Tags
artificial-intelligence · brain-mri · brain-segmentation · deep-learning · freesurfer · medical-imaging · pytorch · unet
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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    Code for "Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs"

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    Deep Learning tools for brain medical images

  • mindGlidebrain-mri · medical-imaging · pytorch

    Brain MRI segmentation for multiple sclerosis — any sequence, any quality. pip install mindglide

  • niftiaibrain-mri · medical-imaging · pytorch

    Train neural nets on 3D images (e.g. MRIs) 🧠

  • BTSC-UNet-ViTmedical-imaging · pytorch · unet

    🧠 BTSC-UNet-ViT— a🚀brain tumor segmentation & classification system combining 🧩Vision Transformers (ViT) for tumor detection and 🎯 UNet for precise pixel-level segmentation from MRI scans.…

sources
  1. api.github.com/repos/NYUMedML/DARTS
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2023-11-01, 69 stars, license reported as GPL-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/33.json→ .entries["darts"]

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