ClinReadNet: A clinical reading-inspired network for low-dose abdominal CT image quality assessment
Quick Answer
ClinReadNet is a novel deep learning framework for low-dose abdominal CT image quality assessment, achieving state-of-the-art performance with Pearson's correlation coefficient of 0.9507.
Quick Take
ClinReadNet is a novel deep learning framework for low-dose abdominal CT image quality assessment, achieving state-of-the-art performance with Pearson's correlation coefficient of 0.9507. It mimics radiologists' reading habits through innovative modules like SOQN and (S)W-MTMSA, and utilizes a hierarchical ranked probability score loss function to enhance accuracy. This advancement significantly impacts clinical imaging practices by improving quality evaluations.
Key Points
- ClinReadNet introduces the Sobel ordinal quality network (SOQN) for edge detail focus.
- The (S)W-MTMSA module replicates radiologists' image-reading processes for better accuracy.
- HRPS loss function combines coarse and fine classification for improved performance.
- Achieved state-of-the-art performance on the LDCTIQAG2023 dataset.
- Correlation coefficients: PLCC 0.9507, SROCC 0.9554, KROCC 0.8629.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 10372v1 Announce Type: new Abstract: In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference IQA) model that mimics doctors' reading habits for evaluating CT image quality has significant practical value.
This paper proposes a novel deep learning-based framework, ClinReadNet, whose design aligns with the clinical reading logic of radiologists: first, it introduces the Sobel ordinal quality network (SOQN) module, which can simultaneously focus on edge details highly relevant to image quality and the quality distribution pattern of the entire image, accurately matching the clinical image-reading judgment habit of "considering both local details and overall context"; second, the framework integrates the (shifted) window multi-scale temperature multi-head self-attention ((S)W-MTMSA) module, which further replicates the radiologists' image-reading process of shifting from overall scanning to local focusing, and accurately locks in regions of interest through multi-sharpness attention; third, it designs the hierarchical ranked probability score (HRPS) loss function, which combines the dual logics of coarse classification and fine classification, while paying attention to the distance information between grading labels, effectively improving the performance of image quality assessment.
Experiments conducted on the LDCTIQAG2023 dataset show that the proposed method achieves the current state-of-the-art (SOTA) performance: the values of Pearson's linear correlation coefficient (PLCC), Spearman's rank-order correlation coefficient (SROCC), and Kendall's rank-order correlation coefficient (KROCC) reach 0. 9507, 0. 9554, and 0. 8629 respectively, with the sum of their absolute values (Score) being 2. 7690, outperforming existing methods.
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