Building robust deep learning-based models requires large quantities of
diverse training data. In this study, we investigate the use of federated
learning (FL) to build medical imaging classification models in a real-world
collaborative setting. Seven clinical institutions from across the world joined
this FL effort to train a model for breast density classification based on
Breast Imaging, Reporting & Data System (BI-RADS). We show that despite
substantial differences among the datasets from all sites (mammography system,
class distribution, and data set size) and without centralizing data, we can
successfully train AI models in federation. The results show that models
trained using FL perform 6.3% on average better than their counterparts trained
on an institute's local data alone. Furthermore, we show a 45.8% relative
improvement in the models' generalizability when evaluated on the other
participating sites' testing data.

本文研究了在真实的合作环境下使用联邦学习（FL）构建医学成像分类模型。通过对世界各地的七个临床机构的数据进行联邦学习，成功地训练了乳腺密度分类模型，其性能比在机构的本地数据训练的模型平均提高了 6.3％，在其他机构的测试数据上表现出了 45.8％的相对改进。

乳腺密度分类的联邦学习：一个实际实现

Federated Learning for Breast Density Classification: A Real-World  Implementation

Breast density classification is an essential part of breast cancer
screening. Although a lot of prior work considered this problem as a task for
learning algorithms, to our knowledge, all of them used small and not
clinically realistic data both for training and evaluation of their models. In
this work, we explore the limits of this task with a data set coming from over
200,000 breast cancer screening exams. We use this data to train and evaluate a
strong convolutional neural network classifier. In a reader study, we find that
our model can perform this task comparably to a human expert.

通过探索超过 200,000 例乳腺癌筛查检查的数据集，使用强卷积神经网络分类器对乳腺密度分类这一问题进行训练和验证，并在读者研究中发现该模型可以与人类专家相媲美。