Abstract：
The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical Schools\' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location.
摘要：
正确解释乳腺密度对评估乳腺癌风险很重要。人工智能已经被证明能够准确预测乳腺密度，然而,由于不同乳房X线照相术系统的成像特征的差异，使用来自一个系统的数据构建的模型不能很好地推广到其他系统。尽管联邦学习（FL）已经成为一种在不需要共享数据的情况下提高AI通用性的方法，在FL期间从所有训练数据中保留特征的最佳方法是活跃的研究领域。为了探索FL方法论，乳腺密度分类FL挑战与美国放射学会合作主办，哈佛医学院\'大众将军布莱根,科罗拉多大学,NVIDIA,和美国国立卫生研究院国家癌症研究所。挑战参与者能够提交能够在三个模拟医疗设施上实施FL的码头工人容器，每个包含一个独特的大型乳房X线照相术数据集。乳腺密度FL挑战赛于2022年6月15日至9月5日举行，吸引了来自世界各地的七名决赛入围者。获胜的FL提交在挑战测试数据上达到0.653的线性kappa得分，在外部测试数据集上达到0.413的线性kappa得分。评分与在中心位置的相同数据上训练的模型相当。
