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torchvahadane

imported

software/torchvahadane

Gpu accelerated vahadane stain normalization for Digital Pathology workflows.

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

record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
computational-pathology · deep-learning · digital-pathology · domain-adaptation · domain-shift · gpu-acceleration · pathology · 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.

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    Whole Slide Image segmentation with weakly supervised multiple instance learning on TCGA | MICCAI2020 https://arxiv.org/abs/2004.05024

  • anonymize-slide-python3-guidigital-pathology · pathology

    Delete the label image to deidentify a whole-slide image (WSI) with an optional GUI

  • eovdigital-pathology · pathology

    A lightweight, cross-platform Whole Slide Image (WSI) viewer for digital pathology.

  • eov-annotations-plugindigital-pathology · pathology

    Annotations plugin for EOV, a lightweight WSI viewer.

  • histolabdigital-pathology · pathology

    Library for Digital Pathology Image Processing

sources
  1. api.github.com/repos/cwlkr/torchvahadane
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2024-02-23, 28 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/21.json→ .entries["torchvahadane"]

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