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Fast-nnUNet

imported

software/fast-nnunet

This is the official repository for Fast-nnUNet, a new fast model inference framework based on the nnUNet framework implementation.

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

Fast-nnUNet project image
GitHub preview card for 77even/Fast-nnUNet. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Software & Systems
Subcategory
unknown
License
Apache-2.0(osi)
Status
active
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
2d-segmentation · 3d-segmentation · ct-scans · ct-segmentation · cuda · distillation · medical-image-analysis · medical-image-processing
Regulatory
unknown
built by · 1

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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    [MICCAI 2023] MedNeXt is a fully ConvNeXt architecture for 3D medical image segmentation.

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  • PCRLv2ct-scans · medical-image-analysis

    An official implementation of PCRLv2 (pre-training and fine-tuning code are included).

  • ct-volume-preprocessingct-scans · medical-image-processing

    End-to-end Python CT volume preprocessing pipeline to convert raw DICOMs into clean 3D numpy arrays for ML. From paper Draelos et al. "Machine-Learning-Based Multiple Abnormality Prediction with…

  • MedVisioncuda · medical-image-processing

    Medical Image Vision Operators, such as RoIAlign, DCNv1, DCNv2 and NMS for both 2/3D images.

sources
  1. api.github.com/repos/77even/Fast-nnUNet
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2026-05-12, 24 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/40.json→ .entries["fast-nnunet"]

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