  Deep neural networks (DNNs) have been expanded into medical fields and
triggered the revolution of some medical applications by extracting complex
features and achieving high accuracy and performance, etc. On the contrast, the
large-scale network brings high requirements of both memory storage and
computation resource, especially for portable medical devices and other
embedded systems. In this work, we first train a DNN for pneumonia detection
using the dataset provided by RSNA Pneumonia Detection Challenge. To overcome
hardware limitation for implementing large-scale networks, we develop a
systematic structured weight pruning method with filter sparsity, column
sparsity and combined sparsity. Experiments show that we can achieve up to 36x
compression ratio compared to the original model with 106 layers, while
maintaining no accuracy degradation. We evaluate the proposed methods on an
embedded low-power device, Jetson TX2, and achieve low power usage and high
energy efficiency.
