A very important aspect of this federated medical teaching research is that it is the largest. That’s because it has an “unprecedented” global data set, which has been examined across 71 institutions across six continents. As a result, it has been possible to demonstrate Potential to improve brain tumor detection from 33%.
Federated Learning to Detect Brain Tumors: Penn Medicine Intel
for his part Jason Martin, Principal Engineer, Intel Labsexplained that “Federated learning has enormous potential in many areas, especially healthcare, as our research with Penn Medicine shows. Its ability to protect sensitive information and data is a great opportunity for future studies and collaborations.” Opens the door for.”, especially in cases where the data set. Otherwise it would be inaccessible. Our work with Penn Medicine has the potential to make a positive impact on patients around the world, and we look forward to further research.”
For context, it’s important to mention that data access has long been an issue in healthcare because of state and national data privacy laws, including Health Insurance Portability and Accountability Act (HIPAA), For this reason, it has become nearly impossible for large-scale medical research and data sharing to take place without compromising patients’ health information. In addition, Intel federated learning hardware and software comply with data privacy mandates and preserve data integrity and security through confidential computing.
Artificial intelligence to identify brain tumors
After this line, the result of pen medicine intel is obtained through processing large amounts of data “In a decentralized system using Intel Federated Learning technology paired with Intel Software Guard Extensions (SGX), which removes data sharing barriers that have historically prevented collaboration on similar cancer and disease research,” with Only Intel has explained this in a statement.
It is notable that the system solves many data privacy issues by keeping the raw data within the data subjects’ computer infrastructure and only allowing updates of models computed from that data to be sent to the server. Central or aggregator, not the data itself.
“All the computing power in the world can’t do much without enough data to analyze”
following this line, Rob Enderl, Principal Analyst, Enderl Grouphighlighted that “all the computing power in the world can’t do much without enough data for analysis. The inability to analyze data that has already been captured makes AI promising Enormous medical advances have been significantly delayed. This study of learning shows a viable path forward for federated AI to move forward and reach its potential as the most powerful tool to combat our most serious diseases.”
As an added bonus, Autor Principal Spyridon BakasPhD, assistant professor of pathology and laboratory medicine and radiology at the University of Pennsylvania Perelman School of Medicine, said that “in this study, federated learning shows its potential as a paradigm shift to ensure multi-institutional collaboration as it expands access.” allows. The largest and most diverse glioblastoma patient dataset ever analyzed, while all data is kept within each institution at all times. The more data we can feed into the machine learning model, the more accurate it will be , which in turn could improve our ability to understand and treat even rare diseases like glioblastoma.”