Vision Foundry: 一个用于训练基础视觉AI模型的系统
Vision Foundry: A System for Training Foundational Vision AI Models
AI总结：
Vision Foundry通过无需代码的HIPAA合规平台，提升基础视觉模型在医学领域的应用效能，实现零样本泛化与高精度分割。
摘要：
自监督学习（SSL）利用庞大的未标注医学数据集，但技术障碍限制了临床研究人员的采用。我们介绍了Vision Foundry，一个无需代码、符合HIPAA规定的平台，使预训练、适应和部署基础视觉模型变得民主化。该系统集成了DINO-MX框架，在抽象分布式基础设施复杂性的同时，实施了专门的策略，如Magnification-Aware Distillation（MAD）和Parameter-Efficient Fine-Tuning（PEFT）。我们验证了该平台在神经病理学分割、肺细胞计数估计和冠状动脉钙评分等领域的应用。我们的实验表明，通过Vision Foundry训练的模型在分割保真度和回归准确性方面显著优于通用基线，同时在不同成像协议中表现出鲁棒的零样本泛化能力。通过弥合先进表示学习与实际应用之间的差距，Vision Foundry使领域专家能够以最小的标注开销开发最先进的临床AI工具，将关注点从工程优化转向临床发现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
