An interactive axial feature selection network for medical image classification.

Abstract

To address the differences and correlations between features, as well as to fully utilize the importance of salient semantics in medical image classification tasks, this paper proposes an Interactive Axial Feature Selection Network (IAFSNet), aimed at improving feature representation, effectively filtering noise during classification, thereby enhancing classification performance. The paper introduces a newly designed Feature Interaction Module (FIM), which learns spatial differences between various features and enhances the interdependence and complementarity between local spatial features and global contextual semantics. Additionally, the paper implements a novel Axial Feature Selection Module (AFSM), which filters salient feature semantics from three perspectives: horizontal, vertical, and spatial. By adjusting thresholds, salient features are emphasized while irrelevant noise is eliminated, allowing these key features to cross-aggregate layer by layer and establish interactions among them, ultimately improving classification accuracy. Experimental results on four benchmark datasets demonstrate that the proposed IAFSNet exhibits excellent classification performance and robustness, significantly outperforming many existing classification methods.