提出新数据集与模型，精准评估眼底图像质量。
Acquire Precise and Comparable Fundus Image Quality Score: FTHNet and FQS Dataset
- 构建可连续评分的眼底图像质量数据集FQS
- 新模型FTHNet在该数据集上达0.9423的PLCC和0.9488的SRCC
- 适合医疗AI质检系统部署，计算量小易落地
眼底图像广泛用于眼科诊断，其质量直接影响诊断结果。然而，当前眼底图像质量评估（FIQA）方法受限于数据集不足与算法能力弱，难以满足临床需求。本文针对这一问题，首先构建了新的FIQA数据集FQS，包含2246张眼底图像，每张图像配有0到100的连续均值主观评分（MOS）及三级质量标签；其次提出基于Transformer的超网络模型FTHNet，采用回归而非传统分类方式输出质量分数。通过10折交叉验证，FTHNet在FQS数据集上取得PLCC为0.9423、SRCC为0.9488的优异表现，显著优于现有方法，且参数更少、计算复杂度更低。实验还验证了该模型在自动医学图像质量控制中的部署潜力。
原文摘要 · Abstract (English)
The retinal fundus images are utilized extensively in the diagnosis, and their quality can directly affect the diagnosis results. However, due to the insufficient dataset and algorithm application, current fundus image quality assessment (FIQA) methods are not powerful enough to meet ophthalmologists` demands. In this paper, we address the limitations of datasets and algorithms in FIQA. First, we establish a new FIQA dataset, Fundus Quality Score(FQS), which includes 2246 fundus images with two labels: a continuous Mean Opinion Score varying from 0 to 100 and a three-level quality label. Then, we propose a FIQA Transformer-based Hypernetwork (FTHNet) to solve these tasks with regression results rather than classification results in conventional FIQA works. The FTHNet is optimized for the FIQA tasks with extensive experiments. Results on our FQS dataset show that the FTHNet can give quality scores for fundus images with PLCC of 0.9423 and SRCC of 0.9488, significantly outperforming other methods with fewer parameters and less computation complexity.We successfully build a dataset and model addressing the problems of current FIQA methods. Furthermore, the model deployment experiments demonstrate its potential in automatic medical image quality control. All experiments are carried out with 10-fold cross-validation to ensure the significance of the results.
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