Abstract
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.
Abstract (translated)
基于深度学习的病理图像分析由于网络设计的实际限制而面临独特的挑战。大多数现有的方法直接将计算机视觉模型应用于医疗任务，忽略了病理图像的独特特征。这种不匹配通常会导致计算效率低下，特别是在边缘计算场景中。为了解决这个问题，我们提出了一种新的网络相似性导向初始化（NSDI）策略来提高神经架构搜索（NAS）的稳定性。此外，我们将领域适应引入到一次性NAS中，以更好地处理病理数据集中在染色和语义尺度上的变化。我们在BRACS数据集上的实验表明，我们的方法优于现有方法，在分类性能和临床相关的特征定位方面都表现出更好的效果。
URL
https://arxiv.org/abs/2506.14176
