Uno-Qa: an Unsupervised Anomaly-Aware Framework with Test-Time Clustering for Octa Image Quality Assessment

Juntao Chen; Li Lin; Pujin Cheng; Yijin Huang; Xiaoying Tang

10.1109/ISBI53787.2023.10230810

2023

2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI

1945-7928

978-1-6654-7359-0

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

2023-April

1-5

18-21 April 2023

Cartagena, Colombia

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.

Unsupervised Image Quality Assessment OCTA Anomaly-aware Representation Learning Test-time Clustering

第一

CPCI-S ; EI

20233914806408

Image quality ; Medical imaging ; Quality control

Biomedical Engineering:461.1 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746 ; Quality Assurance and Control:913.3

IEEE

Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China

Juntao Chen,Li Lin,Pujin Cheng,et al. Uno-Qa: an Unsupervised Anomaly-Aware Framework with Test-Time Clustering for Octa Image Quality Assessment[C],2023:1-5.