{
  "generated_at": "2026-08-02",
  "entries": [
    {
      "id": "arch-dataset",
      "name": "ARCH",
      "tagline": "Pathology image-caption dataset drawn from textbooks and articles.",
      "category": "dataset",
      "tasks": [
        "model-training",
        "retrieval-search"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "arteraai-breast",
      "name": "ArteraAI Breast",
      "tagline": "Prognostic AI test using digital pathology images for breast cancer.",
      "category": "commercial-product",
      "tasks": [
        "survival-prediction"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "breast"
      ]
    },
    {
      "id": "arteraai-prostate",
      "name": "ArteraAI Prostate",
      "tagline": "Prognostic AI test using digital pathology images for prostate cancer.",
      "category": "commercial-product",
      "tasks": [
        "survival-prediction",
        "grading"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "prostate"
      ]
    },
    {
      "id": "asap",
      "name": "ASAP",
      "tagline": "Fast whole-slide image viewer with annotation support.",
      "category": "software-viewer",
      "tasks": [
        "annotation",
        "data-management"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "awesome-pathology-vlms",
      "name": "Awesome-Pathology-VLMs",
      "tagline": "Curated list of pathology vision-language models, datasets and benchmarks.",
      "category": "meta",
      "tasks": [
        "education"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "batch-effect-acquisition-site",
      "name": "Predicting Acquisition Site from TCGA Images",
      "tagline": "Independent confirmation that acquisition site leaks into the pixels.",
      "category": "ethics-safety",
      "tasks": [],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "camelyon16",
      "name": "CAMELYON16",
      "tagline": "Lymph node metastasis detection challenge dataset.",
      "category": "dataset",
      "tasks": [
        "classification",
        "tissue-segmentation",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "lymph-node",
        "breast"
      ]
    },
    {
      "id": "cap-ecc",
      "name": "CAP electronic Cancer Checklists (eCC)",
      "tagline": "Machine-readable cancer synoptic reporting protocols.",
      "category": "standard-interop",
      "tasks": [
        "report-writing",
        "data-management"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "cap-wsi-validation-guideline",
      "name": "CAP Guideline — Validating Whole Slide Imaging",
      "tagline": "The reference guideline for validating a WSI system for diagnostic use.",
      "category": "validation-regulatory",
      "tasks": [
        "quality-control"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "cellpose",
      "name": "Cellpose",
      "tagline": "Generalist cell and nucleus segmentation across imaging modalities.",
      "category": "task-specific-model",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology",
        "cytopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "cellvit",
      "name": "CellViT",
      "tagline": "Vision-transformer nucleus segmentation and classification.",
      "category": "task-specific-model",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "claim-checklist",
      "name": "CLAIM",
      "tagline": "Checklist for artificial intelligence in medical imaging.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "clam",
      "name": "CLAM",
      "tagline": "Attention-based multiple-instance learning for weakly supervised whole-slide classification.",
      "category": "library-framework",
      "tasks": [
        "classification",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "cleanslide",
      "name": "CleanSlide",
      "tagline": "Leakage-audited pan-cancer whole-slide multiple-choice benchmark.",
      "category": "benchmark",
      "tasks": [
        "vqa",
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "conch",
      "name": "CONCH",
      "tagline": "Vision-language foundation model for pathology image-text tasks.",
      "category": "vision-language-model",
      "tasks": [
        "classification",
        "retrieval-search",
        "vqa",
        "tissue-segmentation"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "consort-spirit-ai",
      "name": "CONSORT-AI and SPIRIT-AI",
      "tagline": "Trial reporting and protocol guidelines for AI interventions.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "cpath-omni",
      "name": "CPath-Omni",
      "tagline": "Unified multimodal model spanning patch and whole-slide analysis.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "classification",
        "report-writing"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "cpathagent",
      "name": "CPathAgent",
      "tagline": "Agent that navigates a slide the way a pathologist moves a microscope.",
      "category": "agent-mcp",
      "tasks": [
        "vqa",
        "classification",
        "report-writing"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "cytomine",
      "name": "Cytomine",
      "tagline": "Web-based collaborative platform for annotating and analysing whole-slide images.",
      "category": "software-viewer",
      "tasks": [
        "annotation",
        "data-management",
        "education",
        "telepathology"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "decide-ai",
      "name": "DECIDE-AI",
      "tagline": "Reporting guideline for early live clinical evaluation of AI decision support.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "deepspot",
      "name": "DeepSpot",
      "tagline": "Uses spatial context from surrounding tissue to improve expression prediction.",
      "category": "spatial-omics",
      "tasks": [
        "spatial-transcriptomics",
        "molecular-prediction"
      ],
      "subspecialty": [
        "molecular-pathology",
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "dicom-sr",
      "name": "DICOM Structured Reporting",
      "tagline": "Encoding measurements and findings as structured DICOM objects.",
      "category": "standard-interop",
      "tasks": [
        "report-writing",
        "data-management"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "dicom-wsi",
      "name": "DICOM for Whole Slide Imaging",
      "tagline": "The vendor-neutral standard for storing and exchanging pathology slides.",
      "category": "standard-interop",
      "tasks": [
        "wsi-io",
        "data-management",
        "telepathology"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "digital-slide-archive",
      "name": "Digital Slide Archive",
      "tagline": "Server platform for managing, annotating and analysing large slide collections.",
      "category": "software-viewer",
      "tasks": [
        "annotation",
        "data-management",
        "tissue-segmentation"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "dpa-telepathology-resources",
      "name": "Digital Pathology Association",
      "tagline": "Professional body publishing practical digital pathology guidance.",
      "category": "education",
      "tasks": [
        "education",
        "telepathology"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "dr-llava",
      "name": "Dr-LLaVA",
      "tagline": "Instruction tuning grounded in symbolic clinical reasoning.",
      "category": "vision-language-model",
      "tasks": [
        "vqa"
      ],
      "subspecialty": [
        "haematopathology"
      ],
      "organs": [
        "bone-marrow"
      ]
    },
    {
      "id": "eagle-pathology",
      "name": "EAGLE",
      "tagline": "Preference alignment to reduce hallucination in pathology VLMs.",
      "category": "vision-language-model",
      "tasks": [
        "vqa"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "eu-ivdr",
      "name": "EU IVDR 2017/746",
      "tagline": "The regulation governing in vitro diagnostic devices in the European Union.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "fda-gmlp",
      "name": "Good Machine Learning Practice for Medical Device Development",
      "tagline": "Ten guiding principles agreed by the FDA, Health Canada and the MHRA.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "fda-samd-pccp",
      "name": "FDA Predetermined Change Control Plans for AI Devices",
      "tagline": "How a cleared AI device may be updated without a new submission.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "ghist",
      "name": "GHIST",
      "tagline": "Single-cell resolution spatial gene expression from histology.",
      "category": "spatial-omics",
      "tasks": [
        "spatial-transcriptomics",
        "molecular-prediction"
      ],
      "subspecialty": [
        "molecular-pathology",
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "grandqc",
      "name": "GrandQC",
      "tagline": "Tissue detection and multi-class artefact segmentation for whole slides.",
      "category": "task-specific-model",
      "tasks": [
        "quality-control",
        "tissue-segmentation"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "grundium-ocus",
      "name": "Grundium Ocus",
      "tagline": "Portable single-slide scanner at a fraction of the cost of a bulk system.",
      "category": "hardware-lowresource",
      "tasks": [
        "wsi-io",
        "telepathology",
        "education"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "hematogones",
      "name": "Hematogones.com",
      "tagline": "Free browser tools and synoptic reporting templates for laboratory work.",
      "category": "app",
      "tasks": [
        "report-writing",
        "education"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "hest",
      "name": "HEST-1k / HEST-Bench",
      "tagline": "Paired histology and spatial transcriptomics dataset and benchmark.",
      "category": "benchmark",
      "tasks": [
        "spatial-transcriptomics",
        "model-training"
      ],
      "subspecialty": [
        "molecular-pathology",
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "hist2st",
      "name": "Hist2ST / HisToGene",
      "tagline": "Transformer and graph approaches to expression prediction from histology.",
      "category": "spatial-omics",
      "tasks": [
        "spatial-transcriptomics",
        "molecular-prediction"
      ],
      "subspecialty": [
        "molecular-pathology",
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "histomicstk",
      "name": "HistomicsTK",
      "tagline": "Python toolkit for histology image analysis and feature extraction.",
      "category": "library-framework",
      "tasks": [
        "nuclei-segmentation",
        "stain-normalisation",
        "ihc-quantification",
        "wsi-io"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "histoqc",
      "name": "HistoQC",
      "tagline": "Automated quality control for whole-slide images.",
      "category": "task-specific-model",
      "tasks": [
        "quality-control"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "hl7-fhir",
      "name": "HL7 FHIR",
      "tagline": "The interoperability standard for exchanging health data, including reports.",
      "category": "standard-interop",
      "tasks": [
        "data-management",
        "report-writing"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "hovernet",
      "name": "HoVer-Net",
      "tagline": "Simultaneous nucleus segmentation and classification in histology.",
      "category": "task-specific-model",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "colon",
        "breast",
        "any"
      ]
    },
    {
      "id": "hovernext",
      "name": "HoVer-NeXt",
      "tagline": "Faster successor to HoVer-Net for nucleus segmentation and classification.",
      "category": "task-specific-model",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "ibex-galen-second-read",
      "name": "Ibex Galen Second Read",
      "tagline": "AI second-read quality control for biopsy whole-slide images.",
      "category": "commercial-product",
      "tasks": [
        "classification",
        "quality-control"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "prostate",
        "breast"
      ]
    },
    {
      "id": "icd-o-3",
      "name": "ICD-O-3",
      "tagline": "Topography and morphology coding for cancer registries.",
      "category": "standard-interop",
      "tasks": [
        "data-management"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "iec-62304",
      "name": "IEC 62304",
      "tagline": "International standard for medical device software lifecycle processes.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "indica-halo-ap-dx",
      "name": "Indica Labs HALO AP Dx",
      "tagline": "Anatomic pathology workflow and image analysis platform.",
      "category": "commercial-product",
      "tasks": [
        "annotation",
        "data-management",
        "ihc-quantification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "instanseg",
      "name": "InstanSeg",
      "tagline": "Embedding-based cell segmentation that runs acceptably on a CPU.",
      "category": "task-specific-model",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "iso-14971",
      "name": "ISO 14971",
      "tagline": "Risk management standard for medical devices.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "istar",
      "name": "iStar",
      "tagline": "Super-resolution spatial gene expression from histology and spatial transcriptomics.",
      "category": "spatial-omics",
      "tasks": [
        "spatial-transcriptomics",
        "molecular-prediction"
      ],
      "subspecialty": [
        "molecular-pathology",
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "known-failure-modes",
      "name": "Known Failure Modes in Pathology AI",
      "tagline": "A plain-language index of how these systems actually go wrong.",
      "category": "ethics-safety",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "leica-aperio-gt450-dx",
      "name": "Leica Aperio GT 450 DX",
      "tagline": "Whole-slide scanner cleared for primary diagnosis.",
      "category": "commercial-product",
      "tasks": [
        "wsi-io",
        "data-management"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "llava-med",
      "name": "LLaVA-Med",
      "tagline": "Biomedical vision-language assistant trained rapidly from PubMed figures.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "education"
      ],
      "subspecialty": [
        "histopathology",
        "clinical-pathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "loinc",
      "name": "LOINC",
      "tagline": "Universal codes for laboratory observations and report sections.",
      "category": "standard-interop",
      "tasks": [
        "data-management"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "midog",
      "name": "MIDOG Challenge",
      "tagline": "Mitosis detection benchmarked across scanners, tumours and species.",
      "category": "benchmark",
      "tasks": [
        "mitosis-counting",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology",
        "veterinary-pathology"
      ],
      "organs": [
        "breast",
        "skin",
        "any"
      ]
    },
    {
      "id": "musk",
      "name": "MUSK",
      "tagline": "Vision-language foundation model for precision oncology.",
      "category": "vision-language-model",
      "tasks": [
        "classification",
        "retrieval-search",
        "vqa",
        "molecular-prediction"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "nct-crc-he-100k",
      "name": "NCT-CRC-HE-100K",
      "tagline": "Colorectal tissue-type patch classification dataset.",
      "category": "dataset",
      "tasks": [
        "classification",
        "model-training",
        "education"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "colon"
      ]
    },
    {
      "id": "ome-ngff",
      "name": "OME-Zarr / NGFF",
      "tagline": "Cloud-native chunked format for bioimaging, including whole slides.",
      "category": "standard-interop",
      "tasks": [
        "wsi-io",
        "data-management"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "omero",
      "name": "OMERO",
      "tagline": "Image data management server for microscopy, including whole-slide images.",
      "category": "software-viewer",
      "tasks": [
        "data-management",
        "annotation"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "openflexure-microscope",
      "name": "OpenFlexure Microscope",
      "tagline": "Open-source 3D-printed automated microscope for low-resource settings.",
      "category": "hardware-lowresource",
      "tasks": [
        "telepathology",
        "education",
        "annotation"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "openslide",
      "name": "OpenSlide",
      "tagline": "C library with Python bindings for reading proprietary whole-slide formats.",
      "category": "library-framework",
      "tasks": [
        "wsi-io"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "openwsi",
      "name": "OpenWSI",
      "tagline": "Low-cost high-throughput whole slide imaging from open-source hardware.",
      "category": "hardware-lowresource",
      "tasks": [
        "wsi-io"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "owkin-pathology-explorer",
      "name": "Owkin Pathology Explorer",
      "tagline": "Pathology analysis agent reachable over the Model Context Protocol.",
      "category": "agent-mcp",
      "tasks": [
        "classification",
        "tissue-segmentation",
        "survival-prediction",
        "retrieval-search"
      ],
      "subspecialty": [
        "histopathology",
        "molecular-pathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pa-llava",
      "name": "PA-LLaVA",
      "tagline": "Pathology language-vision assistant built on PLIP with two-stage training.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "paige-fullfocus",
      "name": "Paige FullFocus",
      "tagline": "Whole-slide image viewer for primary diagnosis.",
      "category": "commercial-product",
      "tasks": [
        "annotation",
        "data-management"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "paige-prostate",
      "name": "Paige Prostate",
      "tagline": "AI aid for detecting prostate cancer in core needle biopsies.",
      "category": "commercial-product",
      "tasks": [
        "classification",
        "grading"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "prostate"
      ]
    },
    {
      "id": "panda",
      "name": "PANDA",
      "tagline": "Prostate biopsy Gleason grading challenge dataset.",
      "category": "dataset",
      "tasks": [
        "grading",
        "classification",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "prostate"
      ]
    },
    {
      "id": "pannuke",
      "name": "PanNuke",
      "tagline": "Pan-cancer nucleus instance segmentation and classification dataset.",
      "category": "dataset",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathagent",
      "name": "PathAgent",
      "tagline": "Training-free LLM agent that explores whole slides and reasons over findings.",
      "category": "agent-mcp",
      "tasks": [
        "vqa",
        "report-writing"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathai-aisight-dx",
      "name": "PathAI AISight Dx",
      "tagline": "Digital pathology image management and viewing platform.",
      "category": "commercial-product",
      "tasks": [
        "annotation",
        "data-management"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathasst",
      "name": "PathAsst",
      "tagline": "Generative pathology assistant with a domain-adapted CLIP encoder.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathcap",
      "name": "PathCap",
      "tagline": "Pathology image-caption corpus released with PathAsst.",
      "category": "dataset",
      "tasks": [
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathchat",
      "name": "PathChat",
      "tagline": "Multimodal generative copilot for human pathology.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "report-writing",
        "education"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathdojo",
      "name": "PathDojo",
      "tagline": "Practice cases and self-testing for pathologists and trainees.",
      "category": "education",
      "tasks": [
        "education"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathflip",
      "name": "PathFLIP",
      "tagline": "Fine-grained language-image pretraining grounding captions to regions.",
      "category": "vision-language-model",
      "tasks": [
        "classification",
        "retrieval-search",
        "tissue-segmentation"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathgpt",
      "name": "PathGPT",
      "tagline": "Structured pathology prompt library built on explicit prompt patterns.",
      "category": "prompt-skill",
      "tasks": [
        "vqa",
        "education",
        "report-writing"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathinstruct",
      "name": "PathInstruct",
      "tagline": "Pathology instruction-tuning dataset released with PathAsst.",
      "category": "dataset",
      "tasks": [
        "model-training",
        "vqa"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathml",
      "name": "PathML",
      "tagline": "Python library for computational pathology preprocessing and modelling.",
      "category": "library-framework",
      "tasks": [
        "wsi-io",
        "nuclei-segmentation",
        "stain-normalisation",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathmmu",
      "name": "PathMMU",
      "tagline": "Expert-validated multimodal pathology understanding benchmark.",
      "category": "benchmark",
      "tasks": [
        "vqa"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "patho-agenticrag",
      "name": "Patho-AgenticRAG",
      "tagline": "Multimodal retrieval-augmented generation over pathology textbooks.",
      "category": "agent-mcp",
      "tasks": [
        "vqa",
        "retrieval-search",
        "education"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "patho-bench",
      "name": "Patho-Bench",
      "tagline": "Standardised evaluation suite for pathology slide-level foundation models.",
      "category": "benchmark",
      "tasks": [
        "classification",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "patho-r1",
      "name": "Patho-R1",
      "tagline": "Reinforcement-learning pathology reasoner trained on chain-of-thought data.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathologyapps",
      "name": "Apps for Pathologists",
      "tagline": "Differential diagnosis outlines organised by organ system and bench.",
      "category": "app",
      "tasks": [
        "education"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathologyoutlines",
      "name": "PathologyOutlines",
      "tagline": "Free comprehensive online pathology reference.",
      "category": "education",
      "tasks": [
        "education"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathpresenter-clinical-viewer",
      "name": "PathPresenter Clinical Viewer",
      "tagline": "Whole-slide viewer with teaching and conferencing roots.",
      "category": "commercial-product",
      "tasks": [
        "annotation",
        "education",
        "telepathology"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathreasoner-r1",
      "name": "PathReasoner-R1",
      "tagline": "Knowledge-guided structured reasoning for whole-slide pathology.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathvlm-r1",
      "name": "PathVLM-R1",
      "tagline": "GRPO-trained reasoning model for pathology visual questions.",
      "category": "vision-language-model",
      "tasks": [
        "vqa"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "pathvqa",
      "name": "PathVQA",
      "tagline": "Pathology visual question answering benchmark.",
      "category": "benchmark",
      "tasks": [
        "vqa"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "phikon-v2",
      "name": "Phikon-v2",
      "tagline": "Openly licensed pathology tile encoder trained on public cohorts.",
      "category": "foundation-model",
      "tasks": [
        "classification",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "philips-intellisite",
      "name": "Philips IntelliSite Pathology Solution",
      "tagline": "Whole-slide imaging system for primary diagnosis.",
      "category": "commercial-product",
      "tasks": [
        "wsi-io",
        "data-management"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "plip",
      "name": "PLIP",
      "tagline": "Pathology CLIP model trained on image-text pairs mined from social media.",
      "category": "vision-language-model",
      "tasks": [
        "classification",
        "retrieval-search"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "prism",
      "name": "PRISM",
      "tagline": "Slide-level multimodal model generating reports from tile embeddings.",
      "category": "vision-language-model",
      "tasks": [
        "classification",
        "report-writing"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "proscia-concentriq-dx",
      "name": "Proscia Concentriq Dx",
      "tagline": "Digital pathology platform for diagnostic viewing and workflow.",
      "category": "commercial-product",
      "tasks": [
        "annotation",
        "data-management"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "prov-gigapath",
      "name": "Prov-GigaPath",
      "tagline": "Slide-level foundation model with a tile encoder and long-context aggregator.",
      "category": "foundation-model",
      "tasks": [
        "classification",
        "molecular-prediction",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "quilt-1m",
      "name": "Quilt-1M",
      "tagline": "Histopathology image-text pairs mined from educational videos.",
      "category": "dataset",
      "tasks": [
        "model-training",
        "retrieval-search"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "quilt-llava",
      "name": "Quilt-LLaVA",
      "tagline": "Instruction-tuned pathology assistant trained from educational video narration.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "education"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "qupath",
      "name": "QuPath",
      "tagline": "Open-source desktop software for whole-slide image analysis and annotation.",
      "category": "software-viewer",
      "tasks": [
        "annotation",
        "cell-detection",
        "ihc-quantification",
        "tissue-segmentation"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "qupath-cellpose",
      "name": "QuPath Cellpose Extension",
      "tagline": "Brings Cellpose and Omnipose segmentation into QuPath.",
      "category": "qupath-extension",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology",
        "cytopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "qupath-instanseg",
      "name": "QuPath InstanSeg Extension",
      "tagline": "InstanSeg nucleus and cell segmentation inside QuPath.",
      "category": "qupath-extension",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "qupath-stardist",
      "name": "QuPath StarDist Extension",
      "tagline": "Runs StarDist star-convex nucleus detection inside QuPath.",
      "category": "qupath-extension",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology",
        "cytopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "roche-ventana-dp200",
      "name": "Roche Digital Pathology Dx (VENTANA DP 200)",
      "tagline": "Whole-slide scanner and viewing software for diagnostic use.",
      "category": "commercial-product",
      "tasks": [
        "wsi-io",
        "data-management"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "sam-pathology",
      "name": "Segment Anything for Pathology",
      "tagline": "Applying foundation segmentation models to histology.",
      "category": "task-specific-model",
      "tasks": [
        "annotation",
        "nuclei-segmentation",
        "tissue-segmentation"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "sectra-digital-pathology",
      "name": "Sectra Digital Pathology Module",
      "tagline": "Digital pathology module within an enterprise imaging platform.",
      "category": "commercial-product",
      "tasks": [
        "annotation",
        "data-management",
        "telepathology"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "site-specific-signatures",
      "name": "Site-Specific Digital Histology Signatures",
      "tagline": "Evidence that models can learn the submitting site instead of the biology.",
      "category": "ethics-safety",
      "tasks": [],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "slidechat",
      "name": "SlideChat",
      "tagline": "Vision-language assistant operating on whole slides rather than patches.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "report-writing"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "slideflow",
      "name": "Slideflow",
      "tagline": "End-to-end deep learning pipeline for whole-slide images.",
      "category": "library-framework",
      "tasks": [
        "classification",
        "model-training",
        "survival-prediction",
        "molecular-prediction"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "slip-mil",
      "name": "SLIP",
      "tagline": "Slide-level prompt learning for few-shot MIL.",
      "category": "vision-language-model",
      "tasks": [
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "smartpath-r1",
      "name": "SmartPath-R1",
      "tagline": "Mixture-of-experts reasoner unifying ROI and whole-slide tasks.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "snomed-ct",
      "name": "SNOMED CT",
      "tagline": "Clinical terminology underpinning coded pathology reporting.",
      "category": "standard-interop",
      "tasks": [
        "data-management"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "st-data-quality",
      "name": "Data Quality in Histology-to-Expression Prediction",
      "tagline": "How much reported performance is an artefact of the evaluation setup.",
      "category": "ethics-safety",
      "tasks": [
        "spatial-transcriptomics"
      ],
      "subspecialty": [
        "molecular-pathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "st-net",
      "name": "ST-Net",
      "tagline": "Early demonstration that gene expression can be predicted from H&E alone.",
      "category": "spatial-omics",
      "tasks": [
        "spatial-transcriptomics",
        "molecular-prediction"
      ],
      "subspecialty": [
        "molecular-pathology",
        "histopathology"
      ],
      "organs": [
        "breast"
      ]
    },
    {
      "id": "stain-scanner-variability",
      "name": "Stain and Scanner Variability",
      "tagline": "Why a model trained in one laboratory degrades in another.",
      "category": "ethics-safety",
      "tasks": [],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "stard-ai",
      "name": "STARD-AI",
      "tagline": "Reporting guideline for AI diagnostic accuracy studies.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "stardist",
      "name": "StarDist",
      "tagline": "Star-convex polygon nucleus detection.",
      "category": "task-specific-model",
      "tasks": [
        "nuclei-segmentation",
        "cell-detection"
      ],
      "subspecialty": [
        "histopathology",
        "cytopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "tcga",
      "name": "TCGA",
      "tagline": "Pan-cancer whole-slide and molecular data archive.",
      "category": "dataset",
      "tasks": [
        "model-training",
        "molecular-prediction",
        "survival-prediction"
      ],
      "subspecialty": [
        "histopathology",
        "molecular-pathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "tiatoolbox",
      "name": "TIAToolbox",
      "tagline": "Python toolbox for computational pathology pipelines.",
      "category": "library-framework",
      "tasks": [
        "wsi-io",
        "tissue-segmentation",
        "nuclei-segmentation",
        "classification",
        "stain-normalisation"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "titan",
      "name": "TITAN",
      "tagline": "Multimodal whole-slide foundation model producing slide-level embeddings.",
      "category": "foundation-model",
      "tasks": [
        "classification",
        "retrieval-search",
        "report-writing"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "torchstain",
      "name": "torchstain",
      "tagline": "Stain normalisation as a differentiable, GPU-capable operation.",
      "category": "task-specific-model",
      "tasks": [
        "stain-normalisation"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "trident",
      "name": "TRIDENT",
      "tagline": "Toolkit for whole-slide preprocessing and foundation-model feature extraction.",
      "category": "library-framework",
      "tasks": [
        "wsi-io",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "tripod-ai",
      "name": "TRIPOD+AI",
      "tagline": "Reporting guideline for clinical prediction models using machine learning.",
      "category": "validation-regulatory",
      "tasks": [],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "uni",
      "name": "UNI",
      "tagline": "Self-supervised vision foundation model for pathology tile encoding.",
      "category": "foundation-model",
      "tasks": [
        "classification",
        "model-training",
        "retrieval-search"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "vila-mil",
      "name": "ViLa-MIL",
      "tagline": "Dual-scale vision-language MIL for whole-slide classification.",
      "category": "vision-language-model",
      "tasks": [
        "classification"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "virchow2",
      "name": "Virchow2",
      "tagline": "Large pathology vision foundation model trained on a very large slide corpus.",
      "category": "foundation-model",
      "tasks": [
        "classification",
        "model-training"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "webpathology",
      "name": "WebPathology",
      "tagline": "Free image atlas of benign and malignant entities.",
      "category": "education",
      "tasks": [
        "education"
      ],
      "subspecialty": [
        "any"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "wsi-bench",
      "name": "WSI-Bench",
      "tagline": "Whole-slide VQA benchmark organised around morphological description.",
      "category": "benchmark",
      "tasks": [
        "vqa"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "wsi-llava",
      "name": "WSI-LLaVA",
      "tagline": "Multimodal model for whole-slide images with a morphology-aware benchmark.",
      "category": "vision-language-model",
      "tasks": [
        "vqa",
        "report-writing"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "any"
      ]
    },
    {
      "id": "wsinfer",
      "name": "WSInfer",
      "tagline": "Runs pretrained patch classification models across whole slides, with a QuPath front end.",
      "category": "qupath-extension",
      "tasks": [
        "classification",
        "tissue-segmentation"
      ],
      "subspecialty": [
        "histopathology"
      ],
      "organs": [
        "breast",
        "prostate",
        "lung",
        "colon"
      ]
    }
  ]
}