This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using Neural Architecture Search (NAS) methods, specifically Differentiable Architecture Search (DARTS). We present the concept of Implantable Adaptive Cell (IAC), small but powerful modules identified through Partially-Connected DARTS, designed to be injected into the skip connections of an existing and already trained U-shaped model. Our strategy allows for the seamless integration of the IAC into the pre-existing architecture, thereby enhancing its performance without necessitating a complete retraining from scratch. The empirical studies, focusing on medical image segmentation tasks, demonstrate the efficacy of this method. The integration of specialized IAC cells into various configurations of the U-Net model increases segmentation accuracy by almost 2\% points on average for the validation dataset and over 3\% points for the training dataset. The findings of this study not only offer a cost-effective alternative to the complete overhaul of complex models for performance upgrades but also indicate the potential applicability of our method to other architectures and problem domains.

翻译：本文提出了一种创新方法，利用神经架构搜索（NAS）技术，特别是可微分架构搜索（DARTS），来提升医学图像分割领域中预训练神经网络的性能。我们提出了“可植入自适应细胞”（IAC）的概念——通过部分连接DARTS识别出的紧凑而高效的模块，这些模块专为注入到现有已训练U型模型的跳跃连接中而设计。该策略允许将IAC无缝集成至原有架构，从而在不需从头重新训练的前提下提升其性能。聚焦于医学图像分割任务的实证研究表明了该方法的有效性。将特化的IAC细胞集成到U-Net模型的不同配置中后，验证数据集上的分割精度平均提升近2个百分点，训练数据集上的提升超过3个百分点。本研究结果不仅为复杂模型性能升级提供了高性价比替代方案（避免了全面重构），同时也揭示了该方法在其他架构及问题领域的潜在适用性。