提出自适应降噪框架，提升医学影像质量与诊断准确性。
MIND: A Noise-Adaptive Denoising Framework for Medical Images Integrating Multi-Scale Transformer
- 融合多尺度卷积与Transformer，动态感知噪声水平。
- 在多个数据集上显著提升PSNR、SSIM、LPIPS等指标。
- 适合医学图像增强与AI辅助诊断场景使用。
医学影像在疾病诊断中至关重要，其质量直接影响临床判断准确率。然而，低剂量扫描、设备限制和成像伪影常导致医学影像伴随非均匀噪声干扰，严重影响结构识别与病灶检测。本文提出一种集成多尺度卷积与Transformer架构的医学图像自适应降噪模型（MI-ND），引入噪声水平估计器（NLE）和噪声自适应注意力模块（NAAB），实现基于噪声感知的通道-空间注意力调控与跨模态特征融合。在多模态公开数据集上进行系统测试，实验表明该方法在PSNR、SSIM、LPIPS等图像质量指标上显著优于对比方法，并在下游诊断任务中提升F1分数与ROC-AUC，展现出强实用性与推广价值。模型在结构恢复、诊断敏感性和跨模态鲁棒性方面表现突出，为医学图像增强与AI辅助诊疗提供了有效解决方案。
原文摘要 · Abstract (English)
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.
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