论文标题
UNO-QA：一个无监督的异常感知框架，带有测试时间聚类用于八章图像质量评估
UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment
论文作者
论文摘要
医疗图像质量评估（MIQA）是各种医学图像分析应用中的重要先决条件。大多数现有的MIQA算法都是对请求大量注释数据的全面监督。但是，注释医学图像是时必的且劳动力密集的。在本文中，我们提出了一个无监督的异常感知框架，并在测试时间聚类中用于光学相干层析成像血管造影（OCTA）图像质量评估，其中仅在训练阶段只能访问一组高质量的样品。具体而言，提出了一个基于特征的低质量表示模块，以量化八八张图像的质量，然后区分出色的质量和非实心质量。在非实心质量类中，为了进一步将可分析图像与不可缩放的图像区分开，我们执行降低尺寸和聚类的多尺度图像特征，该图像特征由受过训练的八粒质量表示网络提取。在一个可公开访问的数据集SOCTA-3*3-10k上进行了广泛的实验，并成功建立了我们所提出的框架的优势。
Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications. Most existing MIQA algorithms are fully supervised that request a large amount of annotated data. However, annotating medical images is time-consuming and labor-intensive. In this paper, we propose an unsupervised anomaly-aware framework with test-time clustering for optical coherence tomography angiography (OCTA) image quality assessment in a setting wherein only a set of high-quality samples are accessible in the training phase. Specifically, a feature-embedding-based low-quality representation module is proposed to quantify the quality of OCTA images and then to discriminate between outstanding quality and non-outstanding quality. Within the non-outstanding quality class, to further distinguish gradable images from ungradable ones, we perform dimension reduction and clustering of multi-scale image features extracted by the trained OCTA quality representation network. Extensive experiments are conducted on one publicly accessible dataset sOCTA-3*3-10k, with superiority of our proposed framework being successfully established.
