One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification

TL;DR

This paper introduces a novel NAS method tailored for pathological image classification, incorporating network similarity initialization and domain adaptation to enhance stability and performance on medical datasets.

Contribution

It proposes a new NAS approach with network similarity-based initialization and domain adaptation, specifically designed for pathological images, improving efficiency and accuracy.

Findings

Outperforms existing methods on BRACS dataset

Achieves superior classification accuracy

Provides clinically relevant feature localization

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.