Since being named as one of the top 10 breakthrough technologies of 2013, deep learning has hit the headlines repeatedly, with new applications emerging rapidly. In particular, deep learning techniques have proven to be powerful tools for a range of computer vision tasks, including medical imaging.
Accurate diagnosis of disease depends on the acquisition and interpretation of medical images, which is still usually undertaken by humans. Using machines instead is expected to leave less room for human error that is usually due to subjectivity, variations in expertise and opinion of interpreters, and fatigue in physicians.With medical imaging accounting for approximately 90% of all medical data, the application of artificial intelligence to images for more efficient and accurate diagnosis could be a real game-changer. However, the technology is still relatively new, the challenges are to be expected.At the 2017 Deep Learning in Healthcare Summit in London, Ben Glocker, Lecturer in Medical Image Computing at Imperial College London, discussed some of the successes and draw-backs in applying deep learning to medical imaging. View his presentation with slides below. Machines capable of analysing and interpreting medical scans with super-human performance are within reach. Deep learning, in particular, has emerged as a promising tool in our work on automatically detecting brain damage. But getting from the lab into clinical practice comes with great challenges. How do we know when the machine gets it wrong? Can we predict failure, and can we make the machine robust to changes in the clinical data? This talk discusses some of our most recent work that aims to address these critical issues and demonstrate our latest results on deep learning for analysing medical scans.
|The next Deep Learning in Healthcare Summit will take place in Boston on 25-26 May, alongside the annual Deep Learning Summit. Confirmed speakers include David Plans, CEO of BioBeats; Christhian Potes, Senior Scientist at Philips Research; Sergei Azernikov, Machine Learning Lead at Glidewell Laboratories; and Muyinatu Bell, Assistant Professor at John Hopkins University. View more speakers here.
Early Bird passes are available until Friday 31 March. Book your place now.