MD-IQA: Learning Multi-scale Distributed Image Quality Assessment with Semi Supervised Learning for Low Dose CT

• URL: http://arxiv.org/abs/2311.08024v1
• Date: Tue, 14 Nov 2023 09:33:33 GMT
• Title: MD-IQA: Learning Multi-scale Distributed Image Quality Assessment with Semi Supervised Learning for Low Dose CT
• Authors: Tao Song, Ruizhi Hou, Lisong Dai, Lei Xiang
• Abstract summary: Image quality assessment (IQA) plays a critical role in optimizing radiation dose and developing novel medical imaging techniques. Recent deep learning-based approaches have demonstrated strong modeling capabilities and potential for medical IQA. We propose a multi-scale distributions regression approach to predict quality scores by constraining the output distribution.
• Score: 6.158876574189994
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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: https://github.com/zunzhumu/MD-IQA.

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