Intel and Penn Medicine Announce Results of Largest Medical Federated Learning Study |  leader of times

Intel and Penn Medicine Announce Results of Largest Medical Federated Learning Study | leader of times

SANTA CLARA, Calif.–(BUSINESS WIRE)–December 5, 2022–

Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) have used federated learning — a distributed machine learning (ML) artificial intelligence (AI) approach — to help healthcare institutions and international research centers to identify malignant brain tumours. The largest medical federated learning study to date with an unprecedented global dataset examined from 71 institutions on six continents, the project demonstrated the ability to improve brain tumor detection by 33%.

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Using Intel’s Federated Learning technology in combination with Intel Software Guard (SGX) extensions, researchers were able to address many data privacy issues by keeping raw data in data holders’ compute infrastructure and allowing only model updates computed from this data to be sent to a server center or aggregator, not the data itself. (Credit: Intel Corporation)

–Jason Martin, Principal Engineer, Intel Labs

Data accessibility has long been an issue in healthcare due to state and national data privacy laws, including the Health Insurance Portability and Accountability Act (HIPAA). For this reason, large-scale medical research and data sharing has been nearly impossible to achieve without compromising patient health information. Intel’s Federated Learning hardware and software respects data privacy concerns and maintains data integrity, privacy, and security with confidential computing.

The Penn Medicine-Intel result was achieved by processing large volumes of data in a decentralized system using Intel’s Federated Learning technology combined with Intel® Software Guard Extensions (SGX), which removes barriers sharing data that has historically prevented collaboration on cancers and similar diseases. to research. The system addresses many data privacy concerns by keeping the raw data in the data holders’ compute infrastructure and only allowing model updates computed from that data to be sent to a central server or an aggregator, not the data itself.

“All the computing power in the world can’t do much without enough data to analyze,” said Rob Enderle, principal analyst, Enderle Group. “This inability to analyze data that has already been captured has significantly delayed the massive medical breakthroughs promised by AI. This study of federated learning presents a viable path for AI to progress and realize its potential as a most powerful tool to combat our most difficult ailments.

Lead author, Assistant Professor of Pathology and Laboratory Medicine and Radiology at the Perelman School of Medicine, said, “In this study, federated learning shows its potential as a paradigm shift in securing collaborations. multi-institutional by allowing access to the largest and most diversified. dataset of glioblastoma patients never considered in the literature, while all data is kept in each institution at all times. The more data we can feed to machine learning models, the more accurate they become, which can improve our ability to understand and treat even rare diseases, such as glioblastoma.

To advance disease treatment, researchers need access to vast amounts of medical data – in most cases, datasets that exceed the threshold that an institution can produce. Research demonstrates the effectiveness of federated learning at scale and the potential benefits the healthcare industry can realize when multisite data silos are unlocked. Benefits include early detection of disease, which could improve quality of life or increase a patient’s lifespan.

The results of the Penn Medicine-Intel Labs research have been published in the peer-reviewed journal, .

In 2020, Intel and Penn Medicine announced and are using Federated Learning to improve tumor detection and improve treatment outcomes for a rare form of cancer called glioblastoma (GBM), the most common and most common adult brain tumor. lethal with a median survival of only 14 months after standard treatment. Although treatment options have expanded over the past 20 years, there has been no improvement in overall survival rates. The research was funded by the National Cancer Institute of the National Institutes of Health.

Penn Medicine and 71 international healthcare/research institutions have used Intel Federated Learning hardware and software to improve detection of rare cancer boundaries. A new state-of-the-art AI software platform called Federated Tumor Segmentation (FeTS) has been used by radiologists to determine the boundary of a tumor and improve the identification of the “operable region” of tumors or “heart of the tumor”. The radiologists annotated their data and used Open Federated Learning ( ), an open source framework for training machine learning algorithms, to run the federated training. The platform was trained on 3.7 million images of 6,314 GBM patients across six continents, the largest brain tumor dataset to date.

Through this project, Intel Labs and Penn Medicine created a proof of concept for using federated learning to gain insights from data. The solution may significantly affect health care and other fields of study, especially among other types of cancer research. Specifically, Intel developed the OpenFL open-source project to enable customers to take cross-silo federated learning into the real world and confidently deploy it on Intel SGX. Additionally, the new FeTS initiative was established as a collaborative network to provide a platform for continued development and to encourage collaboration with the FeTS platform and Intel’s OpenFL open source toolkit, both available on GitHub.

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PUBLISHED: 05/12/2022 11:00 AM/DISC: 05/12/2022 11:02 AM

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