Detection of malignant brain tumors could be improved by a third

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In a joint study with Intel Labs and the Perelman School of Medicine of the University of Pennsylvania (Penn Medicine), Erasmus MC in Rotterdam has found a way to detect malignant brain tumors more quickly. The researchers used a new technique for this: federated learning. That is a way of machine learning (ML) with artificial intelligence (AI). The project showed that it is possible to improve brain tumor detection by a third, the organizations said in a press release.

It is the largest medical federated learning study to date, examining an unprecedented global dataset from 71 institutions on six continents.

“Federated learning has tremendous potential in many fields, especially healthcare,” said Jason Martin, chief engineer, Intel Labs. “The ability to protect sensitive information and data opens the door for future studies and collaboration, especially in cases where datasets would otherwise be inaccessible.”

Making data accessible

Data accessibility has long been an issue in healthcare due to national data protection laws, including the General Data Protection Regulation (GDPR). This made it almost impossible to realize medical research and data exchange on a large scale without compromising patient privacy. Intel’s federated learning hardware and software meet data privacy requirements and protect data integrity, privacy, and security through confidential computing.


The Penn Medicine-Intel result was achieved by processing large amounts of data in a decentralized system. This was done using Intel federated learning technology in combination with Intel® Software Guard Extensions (SGX). This technology removes barriers to data sharing that have previously stood in the way of collaboration in similar cancer and disease research. The system addresses many data privacy concerns by keeping the raw data within its own hospital network and only allowing model updates calculated from that data to be sent to a central server or aggregator, not the raw data .

Personalize a treatment

“From Erasmus MC, we were able to contribute to improving automatic tumor detection through this federated learning study, without having to send patient data,” explain radiologist Prof. Dr. Smits and biomedical researcher Dr. Van der Voort of Erasmus MC. “Automatic tumor detection is an important step for personalizing and monitoring a treatment, and to develop this methodology it is essential to use data from many different institutions. With this collaboration, we were able to do that easily, while retaining control over our data.”

Breakthrough in secure collaborations

“Federated learning offers a breakthrough in ensuring secure multi-institutional collaborations. It enables access to the largest and most diverse data set ever seen in the literature. All data is kept within each institution at all times,” said senior author Spyridon Bakas, PhD, assistant professor of Pathology & Laboratory Medicine, and Radiology, at the University of Pennsylvania Perelman School of Medicine. “The more data we can feed into machine learning models, the more accurate they become. That in turn will improve our ability to understand and treat even rare diseases, such as glioblastoma.”

Unlocking data silos

To improve the treatment of disease, researchers need access to large amounts of medical data – in most cases, datasets that exceed the threshold that one institution can produce. The research demonstrates the effectiveness of federated learning at scale and the potential benefits healthcare can realize when multisite data silos are opened up. Benefits include early detection of disease, which can improve quality of life or extend a patient’s lifespan.

The results of the study have been published in the journal, Nature Communications.


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