{
    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_hf_20250321.json",
    "version": "1.0.0",
    "changelog": {
        "1.0.0": "initial release of EXAONE Path 2.0"
    },
    "monai_version": "1.4.0",
    "pytorch_version": "2.4.0",
    "numpy_version": "1.24.4",
    "required_packages_version": {
        "torch": "2.4.0",
        "torchvision": "0.15.0",
        "torchstain": "1.3.0",
        "pillow": "10.0.0",
        "huggingface_hub": "0.24.2",
        "transformers": "4.43.3"
    },
    "supported_apps": {
        "exaonepath": ""
    },
    "name": "EXAONE Path 2.0",
    "task": "Pathology Foundation Model and Feature Extraction",
    "description": "EXAONE Path 2.0, a pathology foundation model that learns patch-level representations under direct slide-level supervision. Using only 37k WSIs for training, EXAONE Path 2.0 achieves state-of-the-art average performance across 10 biomarker prediction tasks, demonstrating remarkable data efficiency.",
    "authors": "LG AI Research",
    "copyright": "LG AI Research",
    "data_source": "Large-scale collection of pathology WSIs processed into patches",
    "data_type": "WSI patches",
    "image_classes": "RGB pathology image patches",
    "huggingface_model_id": "LGAI-EXAONE/EXAONE-Path-2.0",
    "huggingface_url": "https://huggingface.co/LGAI-EXAONE/EXAONE-Path-2.0",
    "intended_use": "Research and clinical support for pathology image analysis",
    "references": [
        "PYEON, Myeongjang, et al. EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision. arXiv preprint arXiv:2507.06639, 2025."
    ]
}