MD-IQA: Learning Multi-scale Distributed Image Quality Assessment with
  Semi Supervised Learning for Low Dose CT
Tao Song Ruizhi Hou Lisong Dai Lei Xiang
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.