OsteoCAD让医院无需高端设备也能用AI做骨肿瘤分割
OsteoCAD: A Human-in-the-Loop Cloud-Edge Framework for Bone Tumor Segmentation
- 云边协同架构，本地低配设备也能调用远程GPU算力
- 全流程集成工具链，从数据到分割仅需简单操作
- 实测验证：墨西哥医院成功实现骨肿瘤自动分割
人工智能与深度学习显著推动了医学影像分析的发展，但许多医疗机构受限于计算资源和专业人才，难以应用。为此，我们提出OsteoCAD——一个模块化的eHealth框架，旨在将深度学习工具民主化，便于临床落地。该框架通过一体化、易用的界面，提供从数据集构建、预处理、模型训练到推理的端到端能力，并通过安全连接远程GPU基础设施，缓解本地硬件限制。我们在墨西哥开展真实案例研究，聚焦大范围骨肿瘤分割任务，结果表明该框架可在无需高级技术背景或复杂本地配置的情况下，实现基于深度学习的eHealth解决方案。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adopt them due to limited com- putational resources and specialized expertise. To address these barriers, we introduce OsteoCAD, a modular eHealth framework that democratizes access to DL tools in clinical practice. Osteo- CAD delivers end-to-end DL capabilities-from dataset creation and preprocessing to model training and inference-through an integrated and user-friendly interface. To mitigate local hardware constraints, the framework securely connects to remote GPU infrastructures. We validate OsteoCAD's feasibility through a real-world case study in Mexico focused on large bone tumor segmentation. The results demonstrate the framework's ability to enable DL-powered eHealth solutions without demanding ad- vanced technical expertise or complex local configurations.
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