Bractivate: Dendritic Branching in Segmentation Neural Architecture Search

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. Neurons generate new input connections to active neurons, 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: https://tinyurl.com/bractivate.