Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Using Object Storage Technology vs Vendor Neutral Archives for an Image Data Repository Infrastructure

The intent of this project was to use object storage and its database, which has the ability to add custom extensible metadata to an imaging object being stored within the system, to harness the power of its search capabilities, and to close the technology gap that healthcare faces. This creates a non-disruptive tool that can be used natively by both legacy systems and the healthcare systems of today which leverage more advanced storage technologies. The base infrastructure can be populated alongside current workflows without any interruption to the delivery of services. In certain use cases, this technology can be seen as a true alternative to the VNA (Vendor Neutral Archive) systems implemented by healthcare today. The scalability, security, and ability to process complex objects makes this more than just storage for image data and a commodity to be consumed by PACS (Picture Archiving and Communication System) and workstations. Object storage is a smart technology that can be leveraged to create vendor independence, standards compliance, and a data repository that can be mined for truly relevant content by adding additional context to search capabilities. This functionality can lead to efficiencies in workflow and a wealth of minable data to improve outcomes into the future.