Abstract
Image quality assessment (IQA) plays a critical role in optimizing radiation dose and developing novel medical imaging techniques in computed tomography (CT). Traditional IQA methods relying on hand-crafted features have limitations in summarizing the subjective perceptual experience of image quality. Recent deep learning-based approaches have demonstrated strong modeling capabilities and potential for medical IQA, but challenges remain regarding model generalization and perceptual accuracy. In this work, we propose a multi-scale distributions regression approach to predict quality scores by constraining the output distribution, thereby improving model generalization. Furthermore, we design a dual-branch alignment network to enhance feature extraction capabilities. Additionally, semi-supervised learning is introduced by utilizing pseudo-labels for unlabeled data to guide model training. Extensive qualitative experiments demonstrate the effectiveness of our proposed method for advancing the state-of-the-art in deep learning-based medical IQA. Code is available at: this https URL.
Abstract (translated)
图像质量评估（IQA）在优化辐射剂量和开发计算机断层扫描（CT）中的新医学成像技术方面起着关键作用。传统IQA方法依赖于手工定制特征，在总结图像质量的主观感知经验方面存在局限性。最近基于深度学习的IQA方法显示出强大的建模能力和医学IQA的潜在可能性，但模型的泛化能力和感知准确性仍然存在挑战。在本文中，我们提出了一种多尺度分布回归方法，通过约束输出分布来预测质量分数，从而提高模型的泛化能力。此外，我们还设计了一个双分支对齐网络来增强特征提取能力。此外，通过利用未标记数据的伪标签进行半监督学习，进一步提高了模型的训练效果。大量实验证明，我们提出的IQA方法在推动基于深度学习的医学IQA领域取得了最先进的成果。代码可在此链接下载：https://this URL。
URL
https://arxiv.org/abs/2311.08024
