Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network

Yuanming Lin1; Heng Li1; Haofeng Liu1; Hai Shu2; Zinan Li3; Yan Hu3; Jiang Liu1

10.1109/ISBI53787.2023.10230455

2023

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

Retinal image quality assessment (RIQA) is essential for retinal examinations, as it impacts the certainty of both manual and intelligent diagnosis. Unfortunately, domain shifts, such as the variance of colors and illumination, are prone to confuse RIQA. Though efficient domain adaptation solutions have been proposed, properly transferring RIQA models to new domains remains a troublesome task. This paper presents a domain adaptative RIQA algorithm with knowledge distillation using a competitive teacher-student network (CTSN) to address the above issue. The main structure consists of a teacher network, a student network, and a competition module. The teacher network provides pseudo-labels by adapting source and target domain features, and the student network learns features from target-specific pseudo-labels. The competition module boosts the fine-grained adaptation of RIQA. Comparison experiments and ablation studies demonstrate that our method performs outstandingly in RIQA with domain shifts.

Retinal image quality assessment domain adaptation knowledge distillation

第一

CPCI-S ; EI

WOS:001062050500133

20233914806253

Image quality ; Ophthalmology ; Students

Medicine and Pharmacology:461.6 ; Chemical Operations:802.3

IEEE

1.Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
2.Department of Biostatistics, School of Global Public Health, New York University, New York, USA
3.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Yuanming Lin,Heng Li,Haofeng Liu,et al. Domain Adaptative Retinal Image Quality Assessment with Knowledge Distillation Using Competitive Teacher-Student Network[C],2023:1-5.