Embedded Deployment of Semantic Segmentation in Medicine through
  Low-Resolution Inputs
Erik Ostrowski Muhammad Shafique
When deploying neural networks in real-life situations, the size and
computational effort are often the limiting factors. This is especially true in
environments where big, expensive hardware is not affordable, like in embedded
medical devices, where budgets are often tight. State-of-the-art proposed
multiple different lightweight solutions for such use cases, mostly by changing
the base model architecture, not taking the input and output resolution into
consideration. In this paper, we propose our architecture that takes advantage
of the fact that in hardware-limited environments, we often refrain from using
the highest available input resolutions to guarantee a higher throughput.
Although using lower-resolution input leads to a significant reduction in
computing and memory requirements, it may also incur reduced prediction
quality. Our architecture addresses this problem by exploiting the fact that we
can still utilize high-resolution ground-truths in training. The proposed model
inputs lower-resolution images and high-resolution ground truths, which can
improve the prediction quality by 5.5% while adding less than 200 parameters to
the model. %reducing the frames per second only from 25 to 20. We conduct an
extensive analysis to illustrate that our architecture enhances existing
state-of-the-art frameworks for lightweight semantic segmentation of cancer in
MRI images. We also tested the deployment speed of state-of-the-art lightweight
networks and our architecture on Nvidia's Jetson Nano to emulate deployment in
resource-constrained embedded scenarios.