通过神经元编程进行视网膜血管分割
Retinal Vessel Segmentation via Neuron Programming
AI总结：
本文提出“神经元编程”方法，通过在神经元层面自动搜索神经元类型来增强网络表示能力，并开发超网络预测最优配置，在视网膜血管分割任务中取得竞争力表现。
摘要：
视网膜血管的准确分割在各种眼科疾病的早期诊断和治疗中起着至关重要的作用。为该任务设计网络模型需要细致的调优和大量的实验，以处理视网膜血管微小且交织的形态。为了应对这一挑战，人们开发了神经架构搜索（NAS）方法，以充分探索潜在网络架构的空间并追求最强大的架构。受神经元多样性的启发（这是我们大脑中各种智能行为的生物学基础），本文引入了一种新颖且基础的网络设计方法，称为“神经元编程”，以自动将神经元类型搜索到网络中，从而在神经元层面增强网络的表示能力，这与NAS在架构层面的增强是互补的。此外，为了减轻神经元编程的时间和计算强度，我们开发了一个超网络，利用搜索得出的架构信息来预测最佳的神经元配置。综合实验验证，神经元编程在视网膜血液分割中可以实现具有竞争力的性能，展示了神经元多样性在医学图像分析中的强大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
