Session

DescriptionDeep learning has recently demonstrated performance comparable with, and in some cases superior to, that of human experts in medical image computing. However, deep neural networks are typically very large, which combined with large medical image sizes create various hurdles towards their clinical applications. In medical image computing, not only accuracy but also latency and security are of primary concern, and the hardware platforms are sometimes resource-constrained. The first two talks in this session propose novel solutions for the data acquisition and data processing stages in medical image computing respectively, using hardware-oriented schemes for lower latency, memory footprint and higher performance in embedded platforms. Considering the privacy requirement, the third talk further demonstrates a software/hardware co-exploration framework for hybrid trusted execution environment in medical image computing, preserving privacy while achieving higher efficiency than human experts.