用联邦学习实现冠状动脉狭窄智能评估，保护隐私还高效
FeDETR: a Federated Approach for Stenosis Detection in Coronary Angiography
- 联邦框架下分节点训练检测变压器，中央聚合主干网络
- 在5家医院1001例数据上达到先进水平，减少对大样本依赖
- 适合医疗数据分散场景，兼顾精度与患者隐私保护
评估冠状动脉造影中狭窄程度对患者健康至关重要，因冠状动脉狭窄是心力衰竭的潜在因素。当前用于分级冠状动脉病变的方法（如分数流储备FFR或瞬时波无阻滞比iFR）存在耗时、成本高、侵入性强及观察者间差异等问题。为此，一些深度学习方法被提出以辅助心脏病学家自动估算FFR/iFR值。尽管这些方法有效，但其对大规模数据集的依赖在敏感医疗数据分布环境下难以实现。联邦学习通过聚合多个节点的知识提升模型泛化能力，同时保障数据隐私。本文提出首个用于冠状动脉造影视频狭窄程度评估的联邦检测变压器方法FeDETR，各节点在其本地数据上训练检测变压器（DETR），中央服务器仅联邦聚合网络主干部分。该方法在来自五家医院的1001例造影检查数据集上训练并评估，性能优于现有先进联邦学习方法。
原文摘要 · Abstract (English)
Assessing the severity of stenoses in coronary angiography is critical to the patient's health, as coronary stenosis is an underlying factor in heart failure. Current practice for grading coronary lesions, i.e. fractional flow reserve (FFR) or instantaneous wave-free ratio (iFR), suffers from several drawbacks, including time, cost and invasiveness, alongside potential interobserver variability. In this context, some deep learning methods have emerged to assist cardiologists in automating the estimation of FFR/iFR values. Despite the effectiveness of these methods, their reliance on large datasets is challenging due to the distributed nature of sensitive medical data. Federated learning addresses this challenge by aggregating knowledge from multiple nodes to improve model generalization, while preserving data privacy. We propose the first federated detection transformer approach, FeDETR, to assess stenosis severity in angiography videos based on FFR/iFR values estimation. In our approach, each node trains a detection transformer (DETR) on its local dataset, with the central server federating the backbone part of the network. The proposed method is trained and evaluated on a dataset collected from five hospitals, consisting of 1001 angiographic examinations, and its performance is compared with state-of-the-art federated learning methods.
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