Neural network system has achieved remarkable accuracy in detecting brain hemorrhages

Deep learning and its applications have grown in recent years. Recently, researchers from ETH Zurich used the technique to study dark matter in an industry first. Now, a team working with the University of California, Berkeley and the University of California, San Francisco (UCSF) School of Medicine have trained a convolutional neural network dubbed "PatchFCN" that detects brain hemorrhages with remarkable accuracy. In a paper titled "Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning", the team claims that: We used a single-stage, end-to-end, fully convolutional neural network to achieve accuracy levels comparable to that of highly trained radiologists, including both identification and localization of abnormalities that are missed by radiologists. The team achieved an accuracy of 99 percent, which is the highest recorded accuracy to date for detecting brain hemorrhages. Our algorithm demonstrated the highest accuracy to date for this clinical application, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.991 0.006 for identification of examinations positive for acute intracranial hemorrhage, and also exceeded the performance of 2 of 4 radiologists.