Reproducibility Test Data of 26 MHub Models

This dataset provides a comprehensive collection of test data for 26 models integrated within the MHub platform, a robust solution for deploying, managing, and testing deep learning models tailored for medical imaging. Each model is accompanied by a zip file containing data specific to its "default" workflow. Dataset Composition: Sample Folder: Contains the input data utilized for testing the model’s functionality. Reference Folder: Contains the corresponding output provided by the original model contributor. Test.yml File: This file includes the original contributor’s test setup, which has been accepted by the MHub team. Sample Data Source: The sample images used in this dataset are sourced from public datasets available through the Imaging Data Commons (IDC), a repository that provides access to a wide range of medical imaging data. This ensures that the test cases reflect real-world clinical scenarios, facilitating robust validation of model performance. Purpose and Utility: The primary objective of this dataset is to enable the rigorous testing and validation of model performance within MHub workflows. To assess the performance of a model, users can process the sample data and compare the resulting output to the reference data. Additionally, users may inspect the sample and reference data independently to better understand the input-output structure that defines each model’s workflow. This dataset streamlines the process of model validation. By providing a standardized testing framework, the dataset facilitates reproducible results and accelerates the development of reliable AI models for medical imaging. About MHub: MHub (mhub.ai) is an innovative platform designed to simplify the deployment, management, and testing of deep learning models for medical imaging. It enables researchers and clinicians to integrate AI-based solutions into clinical workflows while ensuring reproducibility and scalability. The platform provides a modular framework where users can execute complex workflows, such as image segmentation, classification, and registration, leveraging state-of-the-art AI models. MHub's goal is to accelerate the development and clinical adoption of medical imaging models by providing a streamlined, user-friendly environment for testing and validating new algorithms. For more information on the platform and its capabilities, visit mhub.ai.本数据集为集成于MHub平台的26款模型提供了完备的测试数据集合。MHub平台是专为医学影像场景打造的深度学习模型部署、管理与测试的可靠解决方案，每个模型均附带一个压缩包，其中包含适配其"default"工作流的专属数据。 数据集构成： 样本文件夹：存放用于测试模型功能的输入数据。 参考文件夹：存放模型原贡献者提供的对应输出结果。 Test.yml文件：该文件包含原贡献者的测试配置，已获MHub团队认可。 样本数据来源： 本数据集所用的样本图像均来自影像数据公共库（Imaging Data Commons, IDC）中的公开数据集，该库提供海量医学影像数据的访问权限。这一设计可确保测试用例贴合真实临床场景，助力对模型性能开展严谨验证。 用途与价值： 本数据集的核心目标是支持在MHub工作流中对模型性能开展严谨测试与验证。用户可通过处理样本数据，并将生成的输出结果与参考数据进行比对，以此评估模型性能。此外，用户还可独立查看样本与参考数据，从而更深入理解定义各模型工作流的输入输出结构。 本数据集简化了模型验证流程，通过提供标准化测试框架，可实现可复现的实验结果，并加速可靠的医学影像AI模型的研发进程。 关于MHub： MHub（mhub.ai）是一款旨在简化医学影像领域深度学习模型部署、管理与测试流程的创新平台。该平台支持研究人员与临床医师将AI解决方案集成至临床工作流中，同时保障实验结果的可复现性与可扩展性。平台提供模块化框架，用户可借助前沿AI模型执行图像分割、分类、配准等复杂工作流。MHub的目标是通过打造精简易用的新算法测试与验证环境，加速医学影像模型的研发与临床落地。 如需了解该平台及其功能的更多信息，请访问mhub.ai。