A team of researchers led by the University of Texas at Austin has developed a new, fully automatic method that combines biophysical models of tumor growth with machine learning algorithms to automatically identify brain tumors.
Researchers are using supercomputers at UT’s Texas Advanced Computing Center as part of the process to analyze magnetic resonance imaging data of patients with gliomas, the most common and aggressive type of primary brain tumor.
