Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening

TL;DR

This paper investigates how camera brand differences affect diabetic retinopathy classification accuracy and proposes a residual-CycleGAN method to adapt models across different camera domains, significantly improving performance.

Contribution

The paper introduces a camera-oriented residual-CycleGAN for domain adaptation to mitigate camera brand differences in fundus images, enhancing DR classification accuracy across diverse camera types.

Findings

Camera brand differences significantly impact classification performance.

The proposed residual-CycleGAN improves accuracy on target camera images.

Labeled camera brands enable further research in domain adaptation.

Abstract

There are extensive researches focusing on automated diabetic reti-nopathy (DR) detection from fundus images. However, the accuracy drop is ob-served when applying these models in real-world DR screening, where the fun-dus camera brands are different from the ones used to capture the training im-ages. How can we train a classification model on labeled fundus images ac-quired from only one camera brand, yet still achieves good performance on im-ages taken by other brands of cameras? In this paper, we quantitatively verify the impact of fundus camera brands related domain shift on the performance of DR classification models, from an experimental perspective. Further, we pro-pose camera-oriented residual-CycleGAN to mitigate the camera brand differ-ence by domain adaptation and achieve increased classification performance on target camera images. Extensive ablation experiments on both the EyePACS da-taset and a private dataset show that the camera brand difference can signifi-cantly impact the classification performance and prove that our proposed meth-od can effectively improve the model performance on the target domain. We have inferred and labeled the camera brand for each image in the EyePACS da-taset and will publicize the camera brand labels for further research on domain adaptation.