2023/11/14 by Tao Song, Song, Tao, Ruizhi Hou +5
Engineering · Medicine · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #Digital Radiography and Breast Imaging #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Radiation Dose and Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.08024
openalex publication_date 2023/11/14 · openalex created_date 2023/11/16 · openalex updated_date 2026/07/28
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: https://github.com/zunzhumu/MD-IQA.