title: Bractivate: Dendritic Branching in Medical Image Segmentation Neural Architecture Search
abstract: Researchers manually compose most neural networks through painstaking experimentation.
  This process is taxing and explores only a limited subset of possible
  architecture. Researchers design architectures to address objectives ranging from low
  space complexity to high accuracy through hours of experimentation. Neural architecture
  search (NAS) is a thriving field for automatically discovering architectures
  achieving these same objectives. Addressing these ever-increasing challenges in
  computing, we take inspiration from the brain because it has the most efficient
  neuronal wiring of any complex structure; its physiology inspires us to propose
  Bractivate, a NAS algorithm inspired by neural dendritic branching. An evolutionary algorithm that adds new skip connection combinations to the most active blocks in the network, propagating salient
  information through the network. We apply our methods to lung x-ray, cell nuclei
  microscopy, and electron microscopy segmentation tasks to highlight Bractivate's robustness.
  Moreover, our ablation studies emphasize dendritic branching's necessity: ablating
  these connections leads to significantly lower model performance. We finally
  compare our discovered architecture with other state-of-the-art UNet models,
  highlighting how efficient skip connections allow Bractivate to achieve comparable
  results with substantially lower space and time complexity, proving how
  Bractivate balances efficiency with performance. We invite you to work with our
  code here: \href{https://tinyurl.com/bractivate}{\textcolor{violet}{https://tinyurl.com/bractivate}}.
authors: Leila Abdelrahman
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