“Studies have shown that, when it comes to tumor boundaries, not only can different physicians have different opinions, but the same physician assessing the same scan can see different tumor boundary definition on one day of the week versus the next,” he said. “Artificial Intelligence allows a physician to have more precise information about where a tumor ends, which directly affects a patient’s treatment and prognosis.”
To test the effectiveness of federated learning and compare it to other machine learning methods, Bakas collaborated with researchers from the University of Texas MD Anderson Cancer Center, Washington University, and the Hillman Cancer Center at the University of Pittsburgh, while Intel Corporation contributed privacy-protecting software to the project.
Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.
The study began with a model that was pre-trained on multi-institutional data from an open-source repository known as the International Brain Tumor Segmentation, or BraTS, challenge. BraTS currently provides a dataset that includes more than 2,600 brain scans captured with magnetic resonance imaging (MRI) from 660 patients. Next, 10 hospitals participated in the study by training AI models with their own patient data. The federated learning technique was then used to aggregate the data and create the consensus model.
The researchers compared federated learning to models trained by single institutions, as well as to other collaborative-learning approaches. The effectiveness of each method was measured by testing them against scans that were annotated manually by neurologists. When compared to a model trained with centralized data that did not protect patient privacy, federated learning was able to perform almost (99 percent) identically. The findings also indicated that increased access to data through data private, multi-institutional collaborations can benefit model performance.
The findings from this study have paved the way for a much larger, ambitious collaboration between Penn Medicine, Intel, and 30 partner institutions, supported by a $1.2 million grant from the National Cancer Institute of the National Institutes of Health that was awarded to Bakas earlier this year. Intel announced in May that Bakas will lead the project, in which the 30 institutions, across nine countries, will use the federated learning approach to train a consensus AI model on brain tumor data. The final goal of the project will be to create an open-source tool for any clinician at any hospital to use. The development of the tool in Penn’s Center for Biomedical Image Computing & Analytics (CBICA) is being led by senior software developer Sarthak Pati, MS.