Abstract
Fetal ultrasound standard plane recognition plays a vital role in ensuring accurate prenatal assessment but remains challenging due to intrinsic factors such as poor tissue contrast, indistinct anatomical boundaries, and variability in image quality caused by operator differences. To address these issues, we introduce a plug-and-play Adaptive Contrast Adjustment Module (ACAM), inspired by how clinicians manually adjust image contrast to highlight clearer structural cues. The proposed module integrates a lightweight, texture-aware subnetwork that learns to generate clinically meaningful contrast parameters, producing multiple contrast-enhanced representations of the same image through a differentiable transformation process. These enhanced views are then fused within subsequent classifiers to enrich discriminative features. Experiments conducted on a multi-center dataset containing 12,400 fetal ultrasound images across six anatomical planes demonstrate consistent performance gains: the accuracy of lightweight models rises by 2.02%, conventional architectures by 1.29%, and state-of-the-art models by 1.15%. The key novelty of ACAM lies in its content-adaptive and clinically aligned contrast modulation, which replaces random preprocessing with physics-guided transformations mimicking sonographers' diagnostic workflows. By leveraging multi-view contrast fusion, our approach enhances robustness against image quality variations and effectively links low-level texture cues with high-level semantic understanding, offering a new framework for medical image analysis in realistic clinical settings. Our code is available at: https://github.com/sysll/ACAM.