Why build your own cancer-sniffing neural network when this 1.3 exaflop supercomputer can do if for you?

The world's fastest deep learning supercomputer is being used to develop algorithms that can help researchers automatically design neural networks for cancer research, according to the Oak Ridge National Laboratory. The World Health Organisation estimates that by 2025, the number of diagnosed new cases of cancer will reach 21.5 million a year, compared to the current number of roughly 18 million. Researchers at Oak Ridge National Laboratory (ORNL) and Stony Brook University reckon that this means doctors will have to analyse about 200 million biopsy scans per year. Neural networks could help ease their workloads, however, so that they can focus more on patient care. There have been several studies describing how computer vision models can be trained to diagnose cancerous cells in the lung or prostate. Although these systems seem promising they're time consuming and expensive to build.