Deep Learning, Multimedia Mining and Medical Image AnalysisChristopher Pal
Polytechnique Montreal, University of Montreal, Canada
|Tuesday 4 August 2015|
|The combination of big data sets and deep learning has sparked a revolution in speech recognition and computer vision. However, insights and developments have been rapidly propagating to other disciplines. In particular, many multimedia processing problems ranging from audio, image, video and language understanding to the analysis of specialized medical imagery have started to see the impact of recent developments in deep learning.
I’ll begin this talk by briefly reviewing some of the initial successes that helped spark the recent wave of interest in deep learning. I'll go on to focus on some more recent advances in multimedia analysis due to the use of deep learning techniques. I'll show how we have used deep learning techniques and combined multiple types of models for different modalities of video to win a competitive challenge on emotion recognition in the wild. I'll also talk about some of our work on mining Wikipedia, Google image search results and YouTube & the role of semi-supervised learning. I'll touch upon our extremely recent work using deep learning methods to produce semantically appropriate and syntactically well formed phrases describing the visual content of video clips.
Obtaining medical data at large scale is much more challenging compared to general multimedia; however, the accurate analysis of medical imagery has the potential to directly affect human health outcomes. This talk will conclude with a discussion of how we have been using deep learning methods for medical image analysis. I'll talk about some of our older and more recent work on segmentation and the BRATS brain tumor segmentation challenge.