The MXNet library is portable and can scale to multiple GPUs and multiple machines. MXNet is supported by major Public Cloud providers including AWS and Azure. Amazon has chosen MXNet as its deep learning framework of choice at AWS. Currently, MXNet is supported by companies like Intel, Nvidia, Dato, Baidu, Microsoft, Wolfram Research, and research institutions such as Carnegie Mellon, MIT, the University of Washington, and the Hong Kong University of Science and Technology.
Abstract: Annotation and segmentation of medical images is a laborious endeavor that can be automated in part via deep learning (DL) techniques. These approaches have achieved state-of-the-art results in generic segmentation tasks, the goal of which is to classify images at the pixel level. The talk is demonstrating this approach to brain MRIs, as a technique for generic segmentation it’s applicable to similar use cases such as analyzing x-rays.
Speaker info: Brad Kenstler is a Data Scientist on the Amazon Machine Learning Solutions Lab team. As part of the ML Solutions Lab, he helps AWS customers leverage ML & AI within their own organization for their own business use-cases and processes. His primary field of interest lies in the intersection of computer vision and deep learning. Outside of work, Brad enjoys listening to heavy metal, tasting new bourbons, and watching the San Francisco 49ers.
6pm – doors open & socializing
7-8pm – presentation
8pm – Q&A and socializing
9pm – closing