This research stems from an initial collaboration between University Hospital Basel in Switzerland and Swiss image analysis software developer 4Quant on a project that aimed to better understand nasopharyngeal cancer, a rare cancer worldwide but unusually common in southern China. In that project, MR images from 14 patients were manually and painstakingly analyzed in a process that took over two hours per patient, said presenter Kevin Mader of 4Quant.
However, the study was ultimately inconclusive due to the small number of patients. A new study recruited 200 patients, but the researchers were overwhelmed by the analysis time of two hours per patient.
"We decided to apply a big-data and deep-learning approach to learn the most important features and how to extract them and classify images accurately," Mader told AuntMinnie.com.
The team concluded that deep learning is capable of incorporating texture, position, and morphological information in very impressive -- if poorly understood -- ways that previous techniques could not, he said.
"Deep learning will not replace radiologists any time soon, but it can accelerate boring, tedious tasks significantly and provide high-level screening to large groups of patients," Mader said. "Understanding and tracing the decisions from a large neural network is a very much open, unsolved problem and is essential for clinical acceptance."
Get all the details by visiting this Wednesday poster presentation.
