Domain-invariant interpretable fundus image quality assessment

Yaxin Shen, Bin Sheng, Ruogu Fang, Huating Li, Ling Dai, Skylar Stolte, Jing Qin, Weiping Jia, Dinggang Shen

Research output: Contribution to journalArticle

Abstract

Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing quality assessment methods focus on the quality of overall image, without interpretable quality feedback for real-time adjustment. Furthermore, these models are often sensitive to the specific imaging devices, and cannot generalize well under different imaging conditions. This paper presents a new multi-task domain adaptation framework to automatically assess fundus image quality. The proposed framework provides interpretable quality assessment with both quantitative scores and quality visualization for potential real-time image recapture with proper adjustment. In particular, the present approach can detect optic disc and fovea structures as landmarks, to assist the assessment through coarse-to-fine feature encoding. The framework also exploit semi-tied adversarial discriminative domain adaptation to make the model generalizable across different data sources. Experimental results demonstrated that the proposed algorithm outperforms different state-of-the-art approaches and achieves an area under the ROC curve of 0.9455 for the overall quality classification.

Original languageEnglish
Article number101654
JournalMedical Image Analysis
Volume61
DOIs
Publication statusPublished - 2020 Apr

Keywords

  • Domain adaptation
  • Fundus image quality assessment
  • Interpretability
  • Multi-task learning

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Domain-invariant interpretable fundus image quality assessment'. Together they form a unique fingerprint.

  • Cite this

    Shen, Y., Sheng, B., Fang, R., Li, H., Dai, L., Stolte, S., Qin, J., Jia, W., & Shen, D. (2020). Domain-invariant interpretable fundus image quality assessment. Medical Image Analysis, 61, [101654]. https://doi.org/10.1016/j.media.2020.101654