Abstract
The core role of medical images in disease diagnosis makes their quality directly affect the accuracy of clinical judgment. However, due to factors such as low-dose scanning, equipment limitations and imaging artifacts, medical images are often accompanied by non-uniform noise interference, which seriously affects structure recognition and lesion detection. This paper proposes a medical image adaptive denoising model (MI-ND) that integrates multi-scale convolutional and Transformer architecture, introduces a noise level estimator (NLE) and a noise adaptive attention module (NAAB), and realizes channel-spatial attention regulation and cross-modal feature fusion driven by noise perception. Systematic testing is carried out on multimodal public datasets. Experiments show that this method significantly outperforms the comparative methods in image quality indicators such as PSNR, SSIM, and LPIPS, and improves the F1 score and ROC-AUC in downstream diagnostic tasks, showing strong prac-tical value and promotional potential. The model has outstanding benefits in structural recovery, diagnostic sensitivity, and cross-modal robustness, and provides an effective solution for medical image enhancement and AI-assisted diagnosis and treatment.
Abstract (translated)
医学影像在疾病诊断中的核心作用使其质量直接影响临床判断的准确性。然而，由于低剂量扫描、设备限制和成像伪影等因素，医学影像常常伴随非均匀噪声干扰，严重影响结构识别和病变检测。本文提出了一种集成多尺度卷积和Transformer架构的医疗图像自适应去噪模型（MI-ND），引入了噪声水平估计器（NLE）和噪声自适应注意模块（NAAB），实现了基于噪声感知的通道空间注意力调节和跨模态特征融合。在多模态公共数据集上进行了系统的测试。实验结果表明，该方法在图像质量指标如PSNR、SSIM和LPIPS方面显著优于比较方法，并提高了下游诊断任务中的F1评分和ROC-AUC值，显示出较强的实际价值和推广潜力。该模型在结构恢复、诊断敏感性和跨模态鲁棒性方面具有突出优势，并为医学影像增强和人工智能辅助诊断与治疗提供了有效的解决方案。
URL
https://arxiv.org/abs/2508.07817
