DGM-DR: Domain Generalization with Mutual Information Regularized
  Diabetic Retinopathy Classification
Aleksandr Matsun Dana O. Mohamed Sharon Chokuwa Muhammad Ridzuan Mohammad Yaqub
The domain shift between training and testing data presents a significant
challenge for training generalizable deep learning models. As a consequence,
the performance of models trained with the independent and identically
distributed (i.i.d) assumption deteriorates when deployed in the real world.
This problem is exacerbated in the medical imaging context due to variations in
data acquisition across clinical centers, medical apparatus, and patients.
Domain generalization (DG) aims to address this problem by learning a model
that generalizes well to any unseen target domain. Many domain generalization
techniques were unsuccessful in learning domain-invariant representations due
to the large domain shift. Furthermore, multiple tasks in medical imaging are
not yet extensively studied in existing literature when it comes to DG point of
view. In this paper, we introduce a DG method that re-establishes the model
objective function as a maximization of mutual information with a large
pretrained model to the medical imaging field. We re-visit the problem of DG in
Diabetic Retinopathy (DR) classification to establish a clear benchmark with a
correct model selection strategy and to achieve robust domain-invariant
representation for an improved generalization. Moreover, we conduct extensive
experiments on public datasets to show that our proposed method consistently
outperforms the previous state-of-the-art by a margin of 5.25% in average
accuracy and a lower standard deviation. Source code available at
https://github.com/BioMedIA-MBZUAI/DGM-DR