针对病理图像特点，用相似性引导初始化提升搜索稳定性与分类效果。
One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification
- 基于网络相似性设计初始化策略，增强神经架构搜索稳定性。
- 在BRACS数据集上实现更优分类性能与临床可解释的特征定位。
- 适合关注医疗图像分析、边缘计算场景下的模型高效设计人群。
基于深度学习的病理图像分析面临网络设计的实际约束。现有方法多直接将计算机视觉模型应用于医学任务，忽视了病理图像的独特特性，导致计算效率低下，尤其在边缘计算场景中。为此，我们提出一种新的网络相似性引导初始化（NSDI）策略，以提升神经架构搜索（NAS）的稳定性。同时，将领域自适应引入单次搜索的NAS中，更好应对染色差异和语义尺度变化。在BRACS数据集上的实验表明，该方法在分类性能和临床相关特征定位方面均优于现有方法。
原文摘要 · Abstract (English)
Deep learning-based pathological image analysis presents unique challenges due to the practical constraints of network design. Most existing methods apply computer vision models directly to medical tasks, neglecting the distinct characteristics of pathological images. This mismatch often leads to computational inefficiencies, particularly in edge-computing scenarios. To address this, we propose a novel Network Similarity Directed Initialization (NSDI) strategy to improve the stability of neural architecture search (NAS). Furthermore, we introduce domain adaptation into one-shot NAS to better handle variations in staining and semantic scale across pathology datasets. Experiments on the BRACS dataset demonstrate that our method outperforms existing approaches, delivering both superior classification performance and clinically relevant feature localization.
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