零代码平台让临床医生也能训练高性能医学影像模型。
Vision Foundry: A System for Training Foundational Vision AI Models
- 无需编程，自动处理分布式训练与数据隐私。
- 在病理分割等任务上显著优于通用基线模型。
- 适合无算法背景的临床研究者快速部署AI工具。
自监督学习（SSL）可利用海量未标注医学数据，但技术门槛高限制了临床研究人员的应用。我们提出Vision Foundry，一个无需编码、符合HIPAA标准的平台，实现基础视觉模型的预训练、适配与部署。系统集成DINO-MX框架，抽象化分布式基础设施复杂性，并引入放大感知蒸馏（MAD）和参数高效微调（PEFT）等策略。在神经病理学分割、肺细胞密度估计和冠状动脉钙化评分等任务中验证效果。实验表明，通过Vision Foundry训练的模型在分割精度和回归准确率上显著优于通用基线，且在不同成像协议下具备强零样本泛化能力。该平台将先进表示学习与实际应用衔接，使领域专家仅以少量标注即可开发顶尖临床AI工具，将注意力从工程优化转向临床发现。
原文摘要 · Abstract (English)
Self-supervised learning (SSL) leverages vast unannotated medical datasets, yet steep technical barriers limit adoption by clinical researchers. We introduce Vision Foundry, a code-free, HIPAA-compliant platform that democratizes pre-training, adaptation, and deployment of foundational vision models. The system integrates the DINO-MX framework, abstracting distributed infrastructure complexities while implementing specialized strategies like Magnification-Aware Distillation (MAD) and Parameter-Efficient Fine-Tuning (PEFT). We validate the platform across domains, including neuropathology segmentation, lung cellularity estimation, and coronary calcium scoring. Our experiments demonstrate that models trained via Vision Foundry significantly outperform generic baselines in segmentation fidelity and regression accuracy, while exhibiting robust zero-shot generalization across imaging protocols. By bridging the gap between advanced representation learning and practical application, Vision Foundry enables domain experts to develop state-of-the-art clinical AI tools with minimal annotation overhead, shifting focus from engineering optimization to clinical discovery.
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