MuTILs_Panoptic
importeddata/mutils-panoptic
Amgad M, Salgado R, Cooper LA. A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs dataset. medRxiv 2022.01.08.22268814.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Data & Standards
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- active
- Maturity
- deployed
- Organization
- PathologyDataScience
- Country
- unknown
- Documentation
- unknown
- Tags
- breast-cancer · computational-pathology · convolutional-neural-networks · deep-learning · digital-pathology · explainable-ai · tumor-infiltrating-lymphocytes · tumor-segmentation
- Regulatory
- unknown
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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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xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
- FAST-Pathologycomputational-pathology · convolutional-neural-networks · digital-pathology
⚡ Open-source software for deep learning-based digital pathology
- GLIMStumor-segmentation
[IMAVIS] The official repository of GLIMS, for 3D volumetric segmentation. Optimized on BraTS 2023 and BTCV datasets.
- NoCodeSegconvolutional-neural-networks · digital-pathology
🔬 Code-free deep segmentation for computational pathology
- api.github.com/repos/PathologyDataScience/MuTILs_Panopticretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-02-06, 28 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
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/v1/entries/52.json→ .entries["mutils-panoptic"]
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