Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks

FINs can be trained to emulate one-or-more weights that are initialized to approximate closed-form statistical features, and may then be integrated within a larger, more complex features. In this work, we perform the first-ever evaluation of FINs network architecture that obtains the power of the feature, without for biomedical image processing tasks. We begin by training a the strict limitations that would result from including the feature set of FINs to imitate six common radiomics features, and then as an input to the model directly. That is, as part of network compare the performance of networks with and without the FINs fine-tuning, the representation captured by the FIN evolves from the for three experimental tasks: COVID-19 detection from CT scans, static feature representation it was first trained to emulate into an brain tumor classification from MRI scans, and brain-tumor segmentation instantiation that is most effective for the task at hand; For instance, from MRI scans; we find that FINs provide best-in-class a FIN that is designed to emulate Shannon's entropy, may evolve performance for all three tasks, while converging faster and more into a Tsalis entropy representation during fine-tuning.