MD-IQA: Learning Multi-scale Distributed Image Quality Assessment with Semi Supervised Learning for Low Dose CT.
CoRR(2023)
摘要
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.
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