Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. Obtaining sufficient data from a single clinical site is challenging and does not address the heterogeneous need for model robustness. Conversely, the collection of data from multiple sites introduces data privacy concerns and potential label noise due to varying annotation standards. To address this dilemma, we explore the use of the federated learning framework while considering label noise. Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction. Specifically, we introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions, enabling the correction of false annotations based on prediction confidence. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites, enhancing the reliability of the correction process. Extensive experiments conducted on two multi-site datasets demonstrate the effectiveness and robustness of our proposed methods, indicating their potential for clinical applications in multi-site collaborations.

翻译：准确测量多发性硬化（MS）在磁共振成像（MRI）中的演变过程，对于理解疾病进展及指导治疗策略至关重要。深度学习模型在自动分割MS病变方面展现出潜力，但精准标注数据的稀缺性阻碍了该领域的发展。从单一临床站点获取足够数据不仅具有挑战性，也无法满足模型鲁棒性的异质性需求。反之，跨多站点数据收集会引发数据隐私问题，且由于不同站点的标注标准差异可能导致标签噪声。为解决这一困境，我们在考虑标签噪声的同时探索了联邦学习框架的应用。该方法在联邦学习范式下，通过结合基于标签校正的噪声鲁棒训练策略，使多临床站点在保障数据隐私的前提下实现协作。具体而言，我们提出了一种解耦硬标签校正（DHLC）策略，该策略考虑了MS病变的不平衡分布与模糊边界，能够基于预测置信度修正错误标注。同时，我们引入了一种中心增强标签校正（CELC）策略，利用聚合后的中心模型作为所有站点的校正教师，提升校正过程的可靠性。在两个多站点数据集上的广泛实验表明，我们提出的方法具有有效性和鲁棒性，凸显了其在多站点协作临床场景中的潜力。