Assessing breast cancer risk from imaging remains a subjective process, in
which radiologists employ simple computer aided detection (CAD) systems or
qualitative visual assessment to estimate breast percent density (PD). Machine
learning (ML) models have become the most promising way to quantify breast
cancer risk for early, accurate, and equitable diagnoses, but training such
models in medical research is often restricted to small, single-institution
data. Since patient demographics and imaging characteristics may vary
considerably across imaging sites, models trained on single-institution data
tend not to generalize well. In response to this problem, MammoDL is proposed,
an open-source software tool that leverages a U-Net architecture to accurately
estimate breast PD and complexity from mammography. With the Open Federated
Learning (OpenFL) library, this solution enables secure training on datasets
across multiple institutions. MammoDL is a leaner, more flexible model than its
predecessors, boasting improved generalization due to federation-enabled
training on larger, more representative datasets.

MammoDL 是一种开源软件工具，采用 U-Net 架构来准确估计乳腺密度和复杂性，通过 Open Federated Learning 在多个机构的数据集上实现了安全训练，与其他模型相比，该模型更轻便、更灵活，因为联邦学习使其在更大、更具代表性的数据集上训练，从而提高了泛化能力。