Maryellen L. Giger

Zilinghan Li, Shilan He, Pranshu Chaturvedi et al.

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://appflx.link

Qiyuan Hu, Heather M. Whitney, Maryellen L. Giger

Deep transfer learning using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown strong predictive power in characterization of breast lesions. However, pretrained convolutional neural networks (CNNs) require 2D inputs, limiting the ability to exploit the rich 4D (volumetric and temporal) image information inherent in DCE-MRI that is clinically valuable for lesion assessment. Training 3D CNNs from scratch, a common method to utilize high-dimensional information in medical images, is computationally expensive and is not best suited for moderately sized healthcare datasets. Therefore, we propose a novel approach using transfer learning that incorporates the 4D information from DCE-MRI, where volumetric information is collapsed at feature level by max pooling along the projection perpendicular to the transverse slices and the temporal information is contained either in second-post contrast subtraction images. Our methodology yielded an area under the receiver operating characteristic curve of 0.89+/-0.01 on a dataset of 1161 breast lesions, significantly outperforming a previous approach that incorporates the 4D information in DCE-MRI by the use of maximum intensity projection (MIP) images.