Feature Imitating Networks Enhance The Performance, Reliability And
  Speed Of Deep Learning On Biomedical Image Processing Tasks
Shangyang Min Mohammad Mahdi Ghassemi Tuka Alhanai
Feature-Imitating-Networks (FINs) are neural networks with weights that are
initialized to approximate closed-form statistical features. In this work, we
perform the first-ever evaluation of FINs for biomedical image processing
tasks. We begin by training a set of FINs to imitate six common radiomics
features, and then compare the performance of networks with and without the
FINs for three experimental tasks: COVID-19 detection from CT scans, brain
tumor classification from MRI scans, and brain-tumor segmentation from MRI
scans; we find that FINs provide best-in-class performance for all three tasks,
while converging faster and more consistently when compared to networks with
similar or greater representational power. The results of our experiments
provide evidence that FINs may provide state-of-the-art performance for a
variety of other biomedical image processing tasks.