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Retinal Vessel Segmentation via Neuron Programming

arXiv 2411.11110 ↗First posted 17 Nov 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

The accurate segmentation of retinal blood vessels plays a crucial role in the early diagnosis and treatment of various ophthalmic diseases. Designing a network model for this task requires meticulous tuning and extensive experimentation to handle the tiny and intertwined morphology of retinal blood vessels. To tackle this challenge, Neural Architecture Search (NAS) methods are developed to fully explore the space of potential network architectures and go after the most powerful one. Inspired by neuronal diversity which is the biological foundation of all kinds of intelligent behaviors in our brain, this paper introduces a novel and foundational approach to neural network design, termed ``neuron programming'', to automatically search neuronal types into a network to enhance a network's representation ability at the neuronal level, which is complementary to architecture-level enhancement done by NAS. Additionally, to mitigate the time and computational intensity of neuron programming, we develop a hypernetwork that leverages the search-derived architectural information to predict optimal neuronal configurations. Comprehensive experiments validate that neuron programming can achieve competitive performance in retinal blood segmentation, demonstrating the strong potential of neuronal diversity in medical image analysis.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 67 references that are in the map. First posted on arXiv 17 Nov 2024.

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References · 22

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10 more
  1. 2020CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imagingcited by 52
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