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tcga_segmentation

imported

software/tcga-segmentation

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

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

tcga_segmentation project image
GitHub preview card for MarvinLer/tcga_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
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
computational-pathology · data-efficient · histopathology · image-segmentation · medical-imaging · miccai · multiple-instance-learning · pathology
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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    [ECCV 2026] A Patient-First Foundation Model for Computational Pathology

  • ACMILcomputational-pathology · histopathology · multiple-instance-learning

    Attention-Challenging Multiple Instance Learning for Whole Slide Image Classification (ECCV2024)

  • CLAMcomputational-pathology · data-efficient

    Open source tools for computational pathology - Nature BME

  • PEPSImiccai · pathology

    [MICCAI 2024] PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI

  • torchstaincomputational-pathology · histopathology · medical-imaging

    Stain normalization tools for histological analysis and computational pathology

sources
  1. api.github.com/repos/MarvinLer/tcga_segmentation
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2021-08-05, 142 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/9.json→ .entries["tcga-segmentation"]

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