histolab-wsi-processing
De la autor
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
Instalare
npx skills add jaechang-hits/SciAgent-Skills --skill histolab-wsi-processing -a claude-code
Se instalează în .claude/skills/histolab-wsi-processing/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
- Folosește sudoScăzut
De ce 84
Securitate95
Proveniență71
Întreținere100
Licență100
Adopție43
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- histolabK-Dense-AI/scientific-agent-skillsExtracts and preprocesses whole-slide histology image tiles with Histolab. Use for WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation. For multiplexed imaging or deep learning inference pipelines, use pathml.Documente★ 54.9K
- pathmlforyourhealth111-pixel/Vibe-SkillsComputational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.Documente★ 3.8K
- alterlab-histolabAlterLab-IEU/AlterLab-Academic-SkillsExtract and preprocess tiles from whole-slide images (WSI) with histolab — OpenSlide-backed slide loading, tissue detection and masks, Random/Grid/Score tile extraction, and image/morphological filters for H&E preprocessing. Use when the user needs lightweight WSI slide preprocessing — building tile datasets for ML training, tissue segmentation, or quick tile-based inspection of histopathology slides. For end-to-end computational-pathology, deep-learning model training, nucleus segmentation, or multiplexed/spatial-proteomics (CODEX, Vectra) pipelines prefer alterlab-pathml instead. Part of the AlterLab Academic Skills suite.Documente★ 78
- alterlab-pathmlAlterLab-IEU/AlterLab-Academic-SkillsRun full computational-pathology workflows with PathML — whole-slide-image (WSI) analysis across 160+ slide formats, multiplexed immunofluorescence (CODEX, Vectra, MERFISH), nucleus segmentation/classification (HoVer-Net, HACTNet), tissue- and cell-graph construction, HDF5 dataset management, and deep-learning model training on pathology data. Use when the user builds end-to-end deep-learning pathology pipelines, analyzes multiplexed or spatial-proteomics slides, or segments nuclei. For lightweight H&E slide preprocessing, tissue masking, or plain Random/Grid/Score tile extraction prefer alterlab-histolab instead. Part of the AlterLab Academic Skills suite.Documente★ 78
- digital-pathologyawslabs/hcls-agent-skillsGenerate correct code for whole-slide image (WSI) analysis using TIAToolbox and foundation models (H-optimus-0, UNI, Prov-GigaPath). Triggers on requests involving whole-slide images, WSI, digital pathology, histopathology, SVS/NDPI/pyramidal TIFF, tissue segmentation, patch extraction, stain normalization, H-optimus-0, TIAToolbox, CAMELYON16/17, SlideGraph, MIL aggregation, HoVer-Net, PanNuke, or SageMaker deployment of pathology models. Produces deterministic commands and Python snippets for slide-info inspection, tissue masking, tile extraction at specified mpp, foundation-model feature embedding, slide-level aggregation, and regulated cloud inference.DevOps★ 67
- scikit-image-processingjaechang-hits/SciAgent-SkillsPython image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.Media★ 383
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