Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data

TL;DR

This paper introduces GUES, a data-centric domain adaptation method for diabetic retinopathy grading that operates without access to source models and handles streaming target data, improving robustness in clinical settings.

Contribution

It proposes a novel generative perturbation approach using VAEs for model-absent domain adaptation in medical imaging, addressing privacy and security concerns.

Findings

GUES outperforms existing methods on DR benchmarks.

It maintains robustness with small batch sizes.

The approach is effective with both frozen and trainable models.

Abstract

Domain shift (the difference between source and target domains) poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy (DR) grading. Despite considering certain clinical requirements, like source data privacy, conventional transfer methods are predominantly model-centered and often struggle to prevent model-targeted attacks. In this paper, we address a challenging Online Model-aGnostic Domain Adaptation (OMG-DA) setting, driven by the demands of clinical environments. This setting is characterized by the absence of the model and the flow of target data. To tackle the new challenge, we propose a novel approach, Generative Unadversarial ExampleS (GUES), which enables adaptation from a data-centric perspective. Specifically, we first theoretically reformulate conventional perturbation optimization in a generative way--learning a perturbation generation function with a latent input variable. During model instantiation, we leverage a Variational AutoEncoder to express this function. The encoder with the reparameterization trick predicts the latent input, whilst the decoder is responsible for the generation. Furthermore, the saliency map is selected as pseudo-perturbation labels. Because it not only captures potential lesions but also theoretically provides an upper bound on the function input, enabling the identification of the latent variable. Extensive comparative experiments on DR benchmarks with both frozen pre-trained models and trainable models demonstrate the superiority of GUES, showing robustness even with small batch size.