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hi-UNI

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software/hi-uni

Official code for Prediction of molecular subtypes for endometrial cancer based on hierarchical foundation model.

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

record
Category
Software & Systems
Subcategory
unknown
License
GPL-3.0(osi)
Status
maintained
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
doi.org/10.1093/bioinformatics/btaf059
Repository
github.com/HaoyuCui/hi-UNI
Documentation
unknown
Tags
digital-pathology · foundation-models · python · pytorch
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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  • DeepSpot2Celldigital-pathology · foundation-models

    DeepSpot2Cell: Predicting virtual single-cell spatial transcriptomics from H&E images using spot-level supervision

  • raw2featuresdigital-pathology · foundation-models

    OME-Zarr whole-slide images to patch- and slide-level foundation-model embeddings - cloud-native, FAIR, model- and backend-agnostic.

  • thunderdigital-pathology · foundation-models

    [NeurIPS25 D&B Spotlight] A tile-level histopathology image understanding benchmark

  • MIRAGEfoundation-models · pytorch

    Official repository of the paper "MIRAGE: A multimodal foundation model and benchmark for comprehensive retinal OCT image analysis", published in npj Digital Medicine (2025).

  • prostate-cancer-detectiondigital-pathology · pytorch

    CNN ensemble for prostate cancer Gleason grading

sources
  1. api.github.com/repos/HaoyuCui/hi-UNI
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2025-05-05, 5 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/25.json→ .entries["hi-uni"]

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